diff --git a/.github/workflows/coverage-badge.yaml b/.github/workflows/coverage-badge.yaml
index 2c3b40fa..05084d31 100644
--- a/.github/workflows/coverage-badge.yaml
+++ b/.github/workflows/coverage-badge.yaml
@@ -9,6 +9,9 @@ on:
jobs:
report:
+ permissions:
+ contents: write
+ pull-requests: write
runs-on: ubuntu-latest
steps:
diff --git a/.github/workflows/e2e_tests.yaml b/.github/workflows/e2e_tests.yaml
index ba59a9e1..44bf1214 100644
--- a/.github/workflows/e2e_tests.yaml
+++ b/.github/workflows/e2e_tests.yaml
@@ -5,13 +5,13 @@ on:
pull_request:
branches:
- main
- - 'release-*'
+ - "release-*"
- ray-jobs-feature
paths-ignore:
- - 'docs/**'
- - '**.adoc'
- - '**.md'
- - 'LICENSE'
+ - "docs/**"
+ - "**.adoc"
+ - "**.md"
+ - "LICENSE"
concurrency:
group: ${{ github.head_ref }}-${{ github.workflow }}
@@ -33,9 +33,9 @@ jobs:
- name: Checkout common repo code
uses: actions/checkout@v4
with:
- repository: 'project-codeflare/codeflare-common'
- ref: 'main'
- path: 'common'
+ repository: "project-codeflare/codeflare-common"
+ ref: "main"
+ path: "common"
- name: Checkout CodeFlare operator repository
uses: actions/checkout@v4
@@ -46,7 +46,7 @@ jobs:
- name: Set Go
uses: actions/setup-go@v5
with:
- go-version-file: './codeflare-operator/go.mod'
+ go-version-file: "./codeflare-operator/go.mod"
cache-dependency-path: "./codeflare-operator/go.sum"
- name: Set up gotestfmt
@@ -76,7 +76,7 @@ jobs:
run: |
cd codeflare-operator
echo Setting up CodeFlare stack
- make setup-e2e
+ make setup-e2e KUEUE_VERSION=v0.13.4 KUBERAY_VERSION=v1.4.0
echo Deploying CodeFlare operator
make deploy -e IMG="${CODEFLARE_OPERATOR_IMG}" -e ENV="e2e"
kubectl wait --timeout=120s --for=condition=Available=true deployment -n openshift-operators codeflare-operator-manager
@@ -95,6 +95,10 @@ jobs:
kubectl create clusterrolebinding sdk-user-namespace-creator --clusterrole=namespace-creator --user=sdk-user
kubectl create clusterrole raycluster-creator --verb=get,list,create,delete,patch --resource=rayclusters
kubectl create clusterrolebinding sdk-user-raycluster-creator --clusterrole=raycluster-creator --user=sdk-user
+ kubectl create clusterrole rayjob-creator --verb=get,list,create,delete,patch --resource=rayjobs
+ kubectl create clusterrolebinding sdk-user-rayjob-creator --clusterrole=rayjob-creator --user=sdk-user
+ kubectl create clusterrole rayjob-status-reader --verb=get,list,patch,update --resource=rayjobs/status
+ kubectl create clusterrolebinding sdk-user-rayjob-status-reader --clusterrole=rayjob-status-reader --user=sdk-user
kubectl create clusterrole appwrapper-creator --verb=get,list,create,delete,patch --resource=appwrappers
kubectl create clusterrolebinding sdk-user-appwrapper-creator --clusterrole=appwrapper-creator --user=sdk-user
kubectl create clusterrole resourceflavor-creator --verb=get,list,create,delete --resource=resourceflavors
@@ -122,7 +126,7 @@ jobs:
pip install poetry
poetry install --with test,docs
echo "Running e2e tests..."
- poetry run pytest -v -s ./tests/e2e -m 'kind and nvidia_gpu' > ${CODEFLARE_TEST_OUTPUT_DIR}/pytest_output.log 2>&1
+ poetry run pytest -v -s ./tests/e2e/ -m 'kind and nvidia_gpu' > ${CODEFLARE_TEST_OUTPUT_DIR}/pytest_output.log 2>&1
env:
GRPC_DNS_RESOLVER: "native"
@@ -146,7 +150,13 @@ jobs:
if: always() && steps.deploy.outcome == 'success'
run: |
echo "Printing KubeRay operator logs"
- kubectl logs -n ray-system --tail -1 -l app.kubernetes.io/name=kuberay | tee ${CODEFLARE_TEST_OUTPUT_DIR}/kuberay.log
+ kubectl logs -n default --tail -1 -l app.kubernetes.io/name=kuberay | tee ${CODEFLARE_TEST_OUTPUT_DIR}/kuberay.log
+
+ - name: Print Kueue controller logs
+ if: always() && steps.deploy.outcome == 'success'
+ run: |
+ echo "Printing Kueue controller logs"
+ kubectl logs -n kueue-system --tail -1 -l control-plane=controller-manager | tee ${CODEFLARE_TEST_OUTPUT_DIR}/kueue.log
- name: Export all KinD pod logs
uses: ./common/github-actions/kind-export-logs
diff --git a/.github/workflows/rayjob_e2e_tests.yaml b/.github/workflows/rayjob_e2e_tests.yaml
new file mode 100644
index 00000000..c4856fd3
--- /dev/null
+++ b/.github/workflows/rayjob_e2e_tests.yaml
@@ -0,0 +1,172 @@
+# rayjob e2e tests workflow for CodeFlare-SDK
+name: rayjob-e2e
+
+on:
+ pull_request:
+ branches:
+ - main
+ - "release-*"
+ - ray-jobs-feature
+ paths-ignore:
+ - "docs/**"
+ - "**.adoc"
+ - "**.md"
+ - "LICENSE"
+
+concurrency:
+ group: ${{ github.head_ref }}-${{ github.workflow }}
+ cancel-in-progress: true
+
+env:
+ CODEFLARE_OPERATOR_IMG: "quay.io/project-codeflare/codeflare-operator:dev"
+
+jobs:
+ kubernetes-rayjob:
+ runs-on: gpu-t4-4-core
+
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v4
+ with:
+ submodules: recursive
+
+ - name: Checkout common repo code
+ uses: actions/checkout@v4
+ with:
+ repository: "project-codeflare/codeflare-common"
+ ref: "main"
+ path: "common"
+
+ - name: Checkout CodeFlare operator repository
+ uses: actions/checkout@v4
+ with:
+ repository: project-codeflare/codeflare-operator
+ path: codeflare-operator
+
+ - name: Set Go
+ uses: actions/setup-go@v5
+ with:
+ go-version-file: "./codeflare-operator/go.mod"
+ cache-dependency-path: "./codeflare-operator/go.sum"
+
+ - name: Set up gotestfmt
+ uses: gotesttools/gotestfmt-action@v2
+ with:
+ token: ${{ secrets.GITHUB_TOKEN }}
+
+ - name: Set up specific Python version
+ uses: actions/setup-python@v5
+ with:
+ python-version: "3.11"
+ cache: "pip" # caching pip dependencies
+
+ - name: Setup NVidia GPU environment for KinD
+ uses: ./common/github-actions/nvidia-gpu-setup
+
+ - name: Setup and start KinD cluster
+ uses: ./common/github-actions/kind
+ with:
+ worker-nodes: 1
+
+ - name: Install NVidia GPU operator for KinD
+ uses: ./common/github-actions/nvidia-gpu-operator
+
+ - name: Deploy CodeFlare stack
+ id: deploy
+ run: |
+ cd codeflare-operator
+ echo Setting up CodeFlare stack
+ make setup-e2e KUEUE_VERSION=v0.13.4 KUBERAY_VERSION=v1.4.0
+ echo Deploying CodeFlare operator
+ make deploy -e IMG="${CODEFLARE_OPERATOR_IMG}" -e ENV="e2e"
+ kubectl wait --timeout=120s --for=condition=Available=true deployment -n openshift-operators codeflare-operator-manager
+ cd ..
+
+ - name: Add user to KinD
+ uses: ./common/github-actions/kind-add-user
+ with:
+ user-name: sdk-user
+
+ - name: Configure RBAC for sdk user with limited permissions
+ run: |
+ kubectl create clusterrole list-ingresses --verb=get,list --resource=ingresses
+ kubectl create clusterrolebinding sdk-user-list-ingresses --clusterrole=list-ingresses --user=sdk-user
+ kubectl create clusterrole namespace-creator --verb=get,list,create,delete,patch --resource=namespaces
+ kubectl create clusterrolebinding sdk-user-namespace-creator --clusterrole=namespace-creator --user=sdk-user
+ kubectl create clusterrole raycluster-creator --verb=get,list,create,delete,patch --resource=rayclusters
+ kubectl create clusterrolebinding sdk-user-raycluster-creator --clusterrole=raycluster-creator --user=sdk-user
+ kubectl create clusterrole rayjob-creator --verb=get,list,create,delete,patch --resource=rayjobs
+ kubectl create clusterrolebinding sdk-user-rayjob-creator --clusterrole=rayjob-creator --user=sdk-user
+ kubectl create clusterrole rayjob-status-reader --verb=get,list,patch,update --resource=rayjobs/status
+ kubectl create clusterrolebinding sdk-user-rayjob-status-reader --clusterrole=rayjob-status-reader --user=sdk-user
+ kubectl create clusterrole appwrapper-creator --verb=get,list,create,delete,patch --resource=appwrappers
+ kubectl create clusterrolebinding sdk-user-appwrapper-creator --clusterrole=appwrapper-creator --user=sdk-user
+ kubectl create clusterrole resourceflavor-creator --verb=get,list,create,delete --resource=resourceflavors
+ kubectl create clusterrolebinding sdk-user-resourceflavor-creator --clusterrole=resourceflavor-creator --user=sdk-user
+ kubectl create clusterrole clusterqueue-creator --verb=get,list,create,delete,patch --resource=clusterqueues
+ kubectl create clusterrolebinding sdk-user-clusterqueue-creator --clusterrole=clusterqueue-creator --user=sdk-user
+ kubectl create clusterrole localqueue-creator --verb=get,list,create,delete,patch --resource=localqueues
+ kubectl create clusterrolebinding sdk-user-localqueue-creator --clusterrole=localqueue-creator --user=sdk-user
+ kubectl create clusterrole list-secrets --verb=get,list --resource=secrets
+ kubectl create clusterrolebinding sdk-user-list-secrets --clusterrole=list-secrets --user=sdk-user
+ kubectl create clusterrole pod-creator --verb=get,list,watch --resource=pods
+ kubectl create clusterrolebinding sdk-user-pod-creator --clusterrole=pod-creator --user=sdk-user
+ kubectl create clusterrole service-reader --verb=get,list,watch --resource=services
+ kubectl create clusterrolebinding sdk-user-service-reader --clusterrole=service-reader --user=sdk-user
+ kubectl create clusterrole port-forward-pods --verb=create --resource=pods/portforward
+ kubectl create clusterrolebinding sdk-user-port-forward-pods-binding --clusterrole=port-forward-pods --user=sdk-user
+ kubectl config use-context sdk-user
+
+ - name: Run RayJob E2E tests
+ run: |
+ export CODEFLARE_TEST_OUTPUT_DIR=${{ env.TEMP_DIR }}
+ echo "CODEFLARE_TEST_OUTPUT_DIR=${CODEFLARE_TEST_OUTPUT_DIR}" >> $GITHUB_ENV
+
+ set -euo pipefail
+ pip install poetry
+ poetry install --with test,docs
+ echo "Running RayJob e2e tests..."
+ poetry run pytest -v -s ./tests/e2e/rayjob/rayjob_lifecycled_cluster_test.py > ${CODEFLARE_TEST_OUTPUT_DIR}/pytest_output_rayjob.log 2>&1
+
+ - name: Switch to kind-cluster context to print logs
+ if: always() && steps.deploy.outcome == 'success'
+ run: kubectl config use-context kind-cluster
+
+ - name: Print Pytest output log
+ if: always() && steps.deploy.outcome == 'success'
+ run: |
+ echo "Printing Pytest output logs"
+ cat ${CODEFLARE_TEST_OUTPUT_DIR}/pytest_output_rayjob.log
+
+ - name: Print CodeFlare operator logs
+ if: always() && steps.deploy.outcome == 'success'
+ run: |
+ echo "Printing CodeFlare operator logs"
+ kubectl logs -n openshift-operators --tail -1 -l app.kubernetes.io/name=codeflare-operator | tee ${CODEFLARE_TEST_OUTPUT_DIR}/codeflare-operator.log
+
+ - name: Print KubeRay operator logs
+ if: always() && steps.deploy.outcome == 'success'
+ run: |
+ echo "Printing KubeRay operator logs"
+ kubectl logs -n default --tail -1 -l app.kubernetes.io/name=kuberay | tee ${CODEFLARE_TEST_OUTPUT_DIR}/kuberay.log
+
+ - name: Print Kueue controller logs
+ if: always() && steps.deploy.outcome == 'success'
+ run: |
+ echo "Printing Kueue controller logs"
+ kubectl logs -n kueue-system --tail -1 -l control-plane=controller-manager | tee ${CODEFLARE_TEST_OUTPUT_DIR}/kueue.log
+
+ - name: Export all KinD pod logs
+ uses: ./common/github-actions/kind-export-logs
+ if: always() && steps.deploy.outcome == 'success'
+ with:
+ output-directory: ${CODEFLARE_TEST_OUTPUT_DIR}
+
+ - name: Upload logs
+ uses: actions/upload-artifact@v4
+ if: always() && steps.deploy.outcome == 'success'
+ with:
+ name: logs
+ retention-days: 10
+ path: |
+ ${{ env.CODEFLARE_TEST_OUTPUT_DIR }}/**/*.log
diff --git a/codecov.yml b/codecov.yml
index 550965e6..4494dcd4 100644
--- a/codecov.yml
+++ b/codecov.yml
@@ -1,3 +1,17 @@
ignore:
- "**/*.ipynb"
- "demo-notebooks/**"
+ - "**/__init__.py"
+
+coverage:
+ precision: 2
+ round: down
+ status:
+ project:
+ default:
+ target: auto
+ threshold: 2.5%
+ patch:
+ default:
+ target: 85%
+ threshold: 2.5%
diff --git a/coverage.svg b/coverage.svg
index a8c7e72a..59d64b37 100644
--- a/coverage.svg
+++ b/coverage.svg
@@ -15,7 +15,7 @@
coverage
coverage
- 92%
- 92%
+ 93%
+ 93%
diff --git a/demo-notebooks/guided-demos/4_rayjob_existing_cluster.ipynb b/demo-notebooks/guided-demos/4_rayjob_existing_cluster.ipynb
new file mode 100644
index 00000000..c0737db0
--- /dev/null
+++ b/demo-notebooks/guided-demos/4_rayjob_existing_cluster.ipynb
@@ -0,0 +1,207 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "9259e514",
+ "metadata": {},
+ "source": [
+ "# Submitting RayJobs against an existing RayCluster\n",
+ "\n",
+ "In this notebook, we will go through the basics of using the SDK to:\n",
+ " * Spin up a Ray cluster with our desired resources\n",
+ " * Verify the status of this cluster\n",
+ " * Submit a RayJob against that cluster\n",
+ " * Verify the status of this job"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "18136ea7",
+ "metadata": {},
+ "source": [
+ "## Creating the RayCluster"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a1c2545d",
+ "metadata": {},
+ "source": [
+ "First, we'll need to import the relevant CodeFlare SDK packages. You can do this by executing the below cell."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "51e18292",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from codeflare_sdk import Cluster, ClusterConfiguration, RayJob"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "649c5911",
+ "metadata": {},
+ "source": [
+ "Run the below `oc login` command using your Token and Server URL. Ensure the command is prepended by `!` and not `%`. This will work when running both locally and within RHOAI."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dc364888",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!oc login --token= --server="
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5581eca9",
+ "metadata": {},
+ "source": [
+ "Next we'll need to initalize our RayCluster and apply it. You can do this be executing the below cell."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3094c60a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cluster = Cluster(ClusterConfiguration(\n",
+ " name='rayjob-cluster',\n",
+ " head_extended_resource_requests={'nvidia.com/gpu':0},\n",
+ " worker_extended_resource_requests={'nvidia.com/gpu':0},\n",
+ " num_workers=2,\n",
+ " worker_cpu_requests=1,\n",
+ " worker_cpu_limits=1,\n",
+ " worker_memory_requests=4,\n",
+ " worker_memory_limits=4,\n",
+ "\n",
+ "))\n",
+ "\n",
+ "cluster.apply()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f3612de2",
+ "metadata": {},
+ "source": [
+ "We can check the status of our cluster by executing the below cell. If it's not up immediately, run the cell a few more times until you see that it's in a 'running' state."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "96d92f93",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cluster.status()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0e2a650",
+ "metadata": {},
+ "source": [
+ "## Submitting the RayJob"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4cf03419",
+ "metadata": {},
+ "source": [
+ "Now we can create the RayJob that we want to submit against the running cluster. The process is quite similar to how we initialize and apply the cluster. \n",
+ "In this context, we need to use the `cluster_name` variable to point it to our existing cluster.\n",
+ "\n",
+ "For the sake of demonstration, the job we'll submit via the `entrypoint` is a single python command. In standard practice this would be pointed to a python training script.\n",
+ "\n",
+ "We'll then call the `submit()` function to run the job against our cluster.\n",
+ "\n",
+ "You can run the below cell to achieve this."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "94edca70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rayjob = RayJob(\n",
+ " job_name=\"sdk-test-job\",\n",
+ " cluster_name=\"rayjob-cluster\",\n",
+ " namespace=\"your-namespace\",\n",
+ " entrypoint=\"python -c 'import time; time.sleep(20)'\",\n",
+ ")\n",
+ "\n",
+ "rayjob.submit()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "30a8899a",
+ "metadata": {},
+ "source": [
+ "We can observe the status of the RayJob in the same way as the RayCluster by invoking the `status()` function via the below cell."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3283b09c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rayjob.status()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9f3c9c9f",
+ "metadata": {},
+ "source": [
+ "This function will output different tables based on the RayJob's current status. You can re-run the cell multiple times to observe the changes as you need to. Once you've observed that the job has been completed, you can shut down the cluster we created earlier by executing the below cell."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5b11e379",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cluster.down()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/demo-notebooks/guided-demos/5_submit_rayjob_cr.ipynb b/demo-notebooks/guided-demos/5_submit_rayjob_cr.ipynb
new file mode 100644
index 00000000..1d9630b7
--- /dev/null
+++ b/demo-notebooks/guided-demos/5_submit_rayjob_cr.ipynb
@@ -0,0 +1,144 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "9259e514",
+ "metadata": {},
+ "source": [
+ "# Submitting a RayJob CR\n",
+ "\n",
+ "In this notebook, we will go through the basics of using the SDK to:\n",
+ " * Define a RayCluster configuration\n",
+ " * Use this configuration alongside a RayJob definition\n",
+ " * Submit the RayJob, and allow Kuberay Operator to lifecycle the RayCluster for the RayJob"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "18136ea7",
+ "metadata": {},
+ "source": [
+ "## Defining and Submitting the RayJob\n",
+ "First, we'll need to import the relevant CodeFlare SDK packages. You can do this by executing the below cell."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "51e18292",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from codeflare_sdk import RayJob, ManagedClusterConfig"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "649c5911",
+ "metadata": {},
+ "source": [
+ "Run the below `oc login` command using your Token and Server URL. Ensure the command is prepended by `!` and not `%`. This will work when running both locally and within RHOAI."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dc364888",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!oc login --token= --server="
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5581eca9",
+ "metadata": {},
+ "source": [
+ "Next we'll need to define the ManagedClusterConfig. Kuberay will use this to spin up a short-lived RayCluster that will only exist as long as the job"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3094c60a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cluster_config = ManagedClusterConfig(\n",
+ " num_workers=2,\n",
+ " worker_cpu_requests=1,\n",
+ " worker_cpu_limits=1,\n",
+ " worker_memory_requests=4,\n",
+ " worker_memory_limits=4,\n",
+ " head_accelerators={'nvidia.com/gpu': 0},\n",
+ " worker_accelerators={'nvidia.com/gpu': 0},\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "02a2b32b",
+ "metadata": {},
+ "source": [
+ "Lastly we can pass the ManagedClusterConfig into the RayJob and submit it. You do not need to worry about tearing down the cluster when the job has completed, that is handled for you!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e905ccea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "job = RayJob(\n",
+ " job_name=\"demo-rayjob\",\n",
+ " entrypoint=\"python -c 'print(\\\"Hello from RayJob!\\\")'\",\n",
+ " cluster_config=cluster_config,\n",
+ " namespace=\"your-namespace\"\n",
+ ")\n",
+ "\n",
+ "job.submit()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f3612de2",
+ "metadata": {},
+ "source": [
+ "We can check the status of our job by executing the below cell. The status may appear as `unknown` for a time while the RayCluster spins up."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "96d92f93",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "job.status()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/demo-notebooks/guided-demos/preview_nbs/4_rayjob_existing_cluster.ipynb b/demo-notebooks/guided-demos/preview_nbs/4_rayjob_existing_cluster.ipynb
new file mode 100644
index 00000000..5348099c
--- /dev/null
+++ b/demo-notebooks/guided-demos/preview_nbs/4_rayjob_existing_cluster.ipynb
@@ -0,0 +1,212 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "9259e514",
+ "metadata": {},
+ "source": [
+ "# Submitting RayJobs against an existing RayCluster\n",
+ "\n",
+ "In this notebook, we will go through the basics of using the SDK to:\n",
+ " * Spin up a Ray cluster with our desired resources\n",
+ " * Verify the status of this cluster\n",
+ " * Submit a RayJob against that cluster\n",
+ " * Verify the status of this job"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "18136ea7",
+ "metadata": {},
+ "source": [
+ "## Creating the RayCluster"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a1c2545d",
+ "metadata": {},
+ "source": [
+ "First, we'll need to import the relevant CodeFlare SDK packages. You can do this by executing the below cell."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "51e18292",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from codeflare_sdk import Cluster, ClusterConfiguration, RayJob, TokenAuthentication"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "649c5911",
+ "metadata": {},
+ "source": [
+ "Execute the below cell to authenticate the notebook via OpenShift."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dc364888",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "auth = TokenAuthentication(\n",
+ " token = \"XXXXX\",\n",
+ " server = \"XXXXX\",\n",
+ " skip_tls=False\n",
+ ")\n",
+ "auth.login()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5581eca9",
+ "metadata": {},
+ "source": [
+ "Next we'll need to initalize our RayCluster and apply it. You can do this be executing the below cell."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3094c60a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cluster = Cluster(ClusterConfiguration(\n",
+ " name='rayjob-cluster',\n",
+ " head_extended_resource_requests={'nvidia.com/gpu':0},\n",
+ " worker_extended_resource_requests={'nvidia.com/gpu':0},\n",
+ " num_workers=2,\n",
+ " worker_cpu_requests=1,\n",
+ " worker_cpu_limits=1,\n",
+ " worker_memory_requests=4,\n",
+ " worker_memory_limits=4,\n",
+ "\n",
+ "))\n",
+ "\n",
+ "cluster.apply()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f3612de2",
+ "metadata": {},
+ "source": [
+ "We can check the status of our cluster by executing the below cell. If it's not up immediately, run the cell a few more times until you see that it's in a 'running' state."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "96d92f93",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cluster.status()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0e2a650",
+ "metadata": {},
+ "source": [
+ "## Creating and Submitting the RayJob"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4cf03419",
+ "metadata": {},
+ "source": [
+ "Now we can create the RayJob that we want to submit against the running cluster. The process is quite similar to how we initialize and apply the cluster. \n",
+ "In this context, we need to use the `cluster_name` variable to point it to our existing cluster.\n",
+ "\n",
+ "For the sake of demonstration, the job we'll submit via the `entrypoint` is a single python command. In standard practice this would be pointed to a python training script.\n",
+ "\n",
+ "We'll then call the `submit()` function to run the job against our cluster.\n",
+ "\n",
+ "You can run the below cell to achieve this."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "94edca70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rayjob = RayJob(\n",
+ " job_name=\"sdk-test-job\",\n",
+ " cluster_name=\"rayjob-cluster\",\n",
+ " namespace=\"rhods-notebooks\",\n",
+ " entrypoint=\"python -c 'import time; time.sleep(20)'\",\n",
+ ")\n",
+ "\n",
+ "rayjob.submit()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "30a8899a",
+ "metadata": {},
+ "source": [
+ "We can observe the status of the RayJob in the same way as the RayCluster by invoking the `submit()` function via the below cell."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3283b09c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rayjob.submit()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9f3c9c9f",
+ "metadata": {},
+ "source": [
+ "This function will output different tables based on the RayJob's current status. You can re-run the cell multiple times to observe the changes as you need to. Once you've observed that the job has been completed, you can shut down the cluster we created earlier by executing the below cell."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5b11e379",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cluster.down()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/poetry.lock b/poetry.lock
index 49e45352..aa315f21 100644
--- a/poetry.lock
+++ b/poetry.lock
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name = "aiohappyeyeballs"
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attrs = ">=17.3.0"
frozenlist = ">=1.1.1"
multidict = ">=4.5,<7.0"
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description = "aiosignal: a list of registered asynchronous callbacks"
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description = "Python package for providing Mozilla's CA Bundle."
optional = false
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pycparser = "*"
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[[package]]
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[[package]]
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+ {file = "rpds_py-0.26.0.tar.gz", hash = "sha256:20dae58a859b0906f0685642e591056f1e787f3a8b39c8e8749a45dc7d26bdb0"},
]
[[package]]
@@ -4020,27 +4093,27 @@ files = [
[[package]]
name = "smart-open"
-version = "7.1.0"
-description = "Utils for streaming large files (S3, HDFS, GCS, Azure Blob Storage, gzip, bz2...)"
+version = "7.3.0.post1"
+description = "Utils for streaming large files (S3, HDFS, GCS, SFTP, Azure Blob Storage, gzip, bz2, zst...)"
optional = false
-python-versions = "<4.0,>=3.7"
+python-versions = "<4.0,>=3.8"
groups = ["main"]
files = [
- {file = "smart_open-7.1.0-py3-none-any.whl", hash = "sha256:4b8489bb6058196258bafe901730c7db0dcf4f083f316e97269c66f45502055b"},
- {file = "smart_open-7.1.0.tar.gz", hash = "sha256:a4f09f84f0f6d3637c6543aca7b5487438877a21360e7368ccf1f704789752ba"},
+ {file = "smart_open-7.3.0.post1-py3-none-any.whl", hash = "sha256:c73661a2c24bf045c1e04e08fffc585b59af023fe783d57896f590489db66fb4"},
+ {file = "smart_open-7.3.0.post1.tar.gz", hash = "sha256:ce6a3d9bc1afbf6234ad13c010b77f8cd36d24636811e3c52c3b5160f5214d1e"},
]
[package.dependencies]
wrapt = "*"
[package.extras]
-all = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "paramiko", "requests", "zstandard"]
+all = ["smart_open[azure,gcs,http,s3,ssh,webhdfs,zst]"]
azure = ["azure-common", "azure-core", "azure-storage-blob"]
gcs = ["google-cloud-storage (>=2.6.0)"]
http = ["requests"]
s3 = ["boto3"]
ssh = ["paramiko"]
-test = ["awscli", "azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "moto[server]", "numpy", "paramiko", "pyopenssl", "pytest", "pytest-benchmark", "pytest-rerunfailures", "requests", "responses", "zstandard"]
+test = ["awscli", "moto[server]", "numpy", "pyopenssl", "pytest", "pytest-rerunfailures", "pytest_benchmark", "responses", "smart_open[all]"]
webhdfs = ["requests"]
zst = ["zstandard"]
@@ -4351,26 +4424,26 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
[[package]]
name = "types-python-dateutil"
-version = "2.9.0.20250516"
+version = "2.9.0.20250708"
description = "Typing stubs for python-dateutil"
optional = false
python-versions = ">=3.9"
groups = ["test"]
files = [
- {file = "types_python_dateutil-2.9.0.20250516-py3-none-any.whl", hash = "sha256:2b2b3f57f9c6a61fba26a9c0ffb9ea5681c9b83e69cd897c6b5f668d9c0cab93"},
- {file = "types_python_dateutil-2.9.0.20250516.tar.gz", hash = "sha256:13e80d6c9c47df23ad773d54b2826bd52dbbb41be87c3f339381c1700ad21ee5"},
+ {file = "types_python_dateutil-2.9.0.20250708-py3-none-any.whl", hash = "sha256:4d6d0cc1cc4d24a2dc3816024e502564094497b713f7befda4d5bc7a8e3fd21f"},
+ {file = "types_python_dateutil-2.9.0.20250708.tar.gz", hash = "sha256:ccdbd75dab2d6c9696c350579f34cffe2c281e4c5f27a585b2a2438dd1d5c8ab"},
]
[[package]]
name = "typing-extensions"
-version = "4.14.0"
+version = "4.14.1"
description = "Backported and Experimental Type Hints for Python 3.9+"
optional = false
python-versions = ">=3.9"
groups = ["main", "test"]
files = [
- {file = "typing_extensions-4.14.0-py3-none-any.whl", hash = "sha256:a1514509136dd0b477638fc68d6a91497af5076466ad0fa6c338e44e359944af"},
- {file = "typing_extensions-4.14.0.tar.gz", hash = "sha256:8676b788e32f02ab42d9e7c61324048ae4c6d844a399eebace3d4979d75ceef4"},
+ {file = "typing_extensions-4.14.1-py3-none-any.whl", hash = "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76"},
+ {file = "typing_extensions-4.14.1.tar.gz", hash = "sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36"},
]
[[package]]
@@ -4417,14 +4490,14 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
[[package]]
name = "urllib3"
-version = "2.4.0"
+version = "2.5.0"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.9"
groups = ["main", "docs", "test"]
files = [
- {file = "urllib3-2.4.0-py3-none-any.whl", hash = "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813"},
- {file = "urllib3-2.4.0.tar.gz", hash = "sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466"},
+ {file = "urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc"},
+ {file = "urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760"},
]
[package.extras]
@@ -4750,4 +4823,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.1"
python-versions = "^3.11"
-content-hash = "6720576cf9ff57c7bb15b97e268bb414218f6a053e7e0a5bdd45d022c0847111"
+content-hash = "9a052e3a816450844fa2cf3427e4660715977aca3b14561d1e9991899624b7c2"
diff --git a/pyproject.toml b/pyproject.toml
index 71610478..6d13354e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -33,6 +33,14 @@ cryptography = "43.0.3"
executing = "1.2.0"
pydantic = ">= 2.10.6"
ipywidgets = "8.1.2"
+python-client = { git = "https://github.com/ray-project/kuberay.git", subdirectory = "clients/python-client", rev = "b2fd91b58c2bbe22f9b4f730c5a8f3180c05e570" }
+
+[[tool.poetry.source]]
+name = "pypi"
+
+[[tool.poetry.source]]
+name = "testpypi"
+url = "https://test.pypi.org/simple/"
[tool.poetry.group.docs]
optional = true
@@ -51,6 +59,10 @@ pytest-mock = "3.11.1"
pytest-timeout = "2.3.1"
jupyterlab = "4.3.1"
+
+[tool.poetry.group.dev.dependencies]
+diff-cover = "^9.6.0"
+
[tool.pytest.ini_options]
filterwarnings = [
"ignore::DeprecationWarning:pkg_resources",
diff --git a/src/codeflare_sdk/__init__.py b/src/codeflare_sdk/__init__.py
index 9ab5c745..a27702e7 100644
--- a/src/codeflare_sdk/__init__.py
+++ b/src/codeflare_sdk/__init__.py
@@ -10,6 +10,8 @@
AWManager,
AppWrapperStatus,
RayJobClient,
+ RayJob,
+ ManagedClusterConfig,
)
from .common.widgets import view_clusters
diff --git a/src/codeflare_sdk/common/kueue/kueue.py b/src/codeflare_sdk/common/kueue/kueue.py
index 00f3364a..a721713e 100644
--- a/src/codeflare_sdk/common/kueue/kueue.py
+++ b/src/codeflare_sdk/common/kueue/kueue.py
@@ -18,6 +18,8 @@
from kubernetes import client
from kubernetes.client.exceptions import ApiException
+from ...common.utils import get_current_namespace
+
def get_default_kueue_name(namespace: str) -> Optional[str]:
"""
@@ -81,7 +83,6 @@ def list_local_queues(
List[dict]:
A list of dictionaries containing the name of the local queue and the available flavors
"""
- from ...ray.cluster.cluster import get_current_namespace
if namespace is None: # pragma: no cover
namespace = get_current_namespace()
diff --git a/src/codeflare_sdk/common/utils/__init__.py b/src/codeflare_sdk/common/utils/__init__.py
index e69de29b..e662bf5e 100644
--- a/src/codeflare_sdk/common/utils/__init__.py
+++ b/src/codeflare_sdk/common/utils/__init__.py
@@ -0,0 +1,7 @@
+"""
+Common utilities for the CodeFlare SDK.
+"""
+
+from .k8s_utils import get_current_namespace
+
+__all__ = ["get_current_namespace"]
diff --git a/src/codeflare_sdk/common/utils/constants.py b/src/codeflare_sdk/common/utils/constants.py
index fcd064d6..7e6147f6 100644
--- a/src/codeflare_sdk/common/utils/constants.py
+++ b/src/codeflare_sdk/common/utils/constants.py
@@ -12,3 +12,4 @@
"3.11": CUDA_PY311_RUNTIME_IMAGE,
"3.12": CUDA_PY312_RUNTIME_IMAGE,
}
+MOUNT_PATH = "/home/ray/scripts"
diff --git a/src/codeflare_sdk/common/utils/k8s_utils.py b/src/codeflare_sdk/common/utils/k8s_utils.py
new file mode 100644
index 00000000..e2e03a5d
--- /dev/null
+++ b/src/codeflare_sdk/common/utils/k8s_utils.py
@@ -0,0 +1,33 @@
+"""
+Kubernetes utility functions for the CodeFlare SDK.
+"""
+
+import os
+from kubernetes import config
+from ..kubernetes_cluster import config_check, _kube_api_error_handling
+
+
+def get_current_namespace(): # pragma: no cover
+ """
+ Retrieves the current Kubernetes namespace.
+
+ Returns:
+ str:
+ The current namespace or None if not found.
+ """
+ if os.path.isfile("/var/run/secrets/kubernetes.io/serviceaccount/namespace"):
+ try:
+ file = open("/var/run/secrets/kubernetes.io/serviceaccount/namespace", "r")
+ active_context = file.readline().strip("\n")
+ return active_context
+ except Exception as e:
+ print("Unable to find current namespace")
+ print("trying to gather from current context")
+ try:
+ _, active_context = config.list_kube_config_contexts(config_check())
+ except Exception as e:
+ return _kube_api_error_handling(e)
+ try:
+ return active_context["context"]["namespace"]
+ except KeyError:
+ return None
diff --git a/src/codeflare_sdk/common/utils/test_demos.py b/src/codeflare_sdk/common/utils/test_demos.py
new file mode 100644
index 00000000..9124cbec
--- /dev/null
+++ b/src/codeflare_sdk/common/utils/test_demos.py
@@ -0,0 +1,57 @@
+# Copyright 2025 IBM, Red Hat
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Tests for demos module.
+"""
+
+import pytest
+import tempfile
+from pathlib import Path
+from unittest.mock import patch, MagicMock
+from codeflare_sdk.common.utils.demos import copy_demo_nbs
+
+
+class TestCopyDemoNbs:
+ """Test cases for copy_demo_nbs function."""
+
+ def test_copy_demo_nbs_directory_exists_error(self):
+ """Test that FileExistsError is raised when directory exists and overwrite=False."""
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Create a subdirectory that will conflict
+ conflict_dir = Path(temp_dir) / "demo-notebooks"
+ conflict_dir.mkdir()
+
+ with pytest.raises(FileExistsError, match="Directory.*already exists"):
+ copy_demo_nbs(dir=str(conflict_dir), overwrite=False)
+
+ def test_copy_demo_nbs_overwrite_true(self):
+ """Test that overwrite=True allows copying to existing directory."""
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Create a subdirectory that will conflict
+ conflict_dir = Path(temp_dir) / "demo-notebooks"
+ conflict_dir.mkdir()
+
+ # Mock the demo_dir to point to a real directory
+ with patch("codeflare_sdk.common.utils.demos.demo_dir", temp_dir):
+ # Should not raise an error with overwrite=True
+ copy_demo_nbs(dir=str(conflict_dir), overwrite=True)
+
+ def test_copy_demo_nbs_default_parameters(self):
+ """Test copy_demo_nbs with default parameters."""
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Mock the demo_dir to point to a real directory
+ with patch("codeflare_sdk.common.utils.demos.demo_dir", temp_dir):
+ # Should work with default parameters
+ copy_demo_nbs(dir=temp_dir, overwrite=True)
diff --git a/src/codeflare_sdk/common/utils/test_k8s_utils.py b/src/codeflare_sdk/common/utils/test_k8s_utils.py
new file mode 100644
index 00000000..fcd0623d
--- /dev/null
+++ b/src/codeflare_sdk/common/utils/test_k8s_utils.py
@@ -0,0 +1,255 @@
+# Copyright 2025 IBM, Red Hat
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Tests for k8s_utils module.
+"""
+
+import pytest
+from unittest.mock import mock_open, patch, MagicMock
+from codeflare_sdk.common.utils.k8s_utils import get_current_namespace
+
+
+class TestGetCurrentNamespace:
+ """Test cases for get_current_namespace function."""
+
+ def test_get_current_namespace_incluster_success(self):
+ """Test successful namespace detection from in-cluster service account."""
+ mock_file_content = "test-namespace\n"
+
+ with patch("os.path.isfile", return_value=True):
+ with patch("builtins.open", mock_open(read_data=mock_file_content)):
+ result = get_current_namespace()
+
+ assert result == "test-namespace"
+
+ def test_get_current_namespace_incluster_file_read_error(self):
+ """Test handling of file read errors when reading service account namespace."""
+ with patch("os.path.isfile", return_value=True):
+ with patch("builtins.open", side_effect=IOError("File read error")):
+ with patch("builtins.print") as mock_print:
+ # Mock config_check to avoid kubeconfig fallback
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils.config_check",
+ side_effect=Exception("Config error"),
+ ):
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils._kube_api_error_handling",
+ return_value=None,
+ ):
+ result = get_current_namespace()
+
+ assert result is None
+ # Should see both error messages: in-cluster failure and kubeconfig fallback
+ mock_print.assert_any_call("Unable to find current namespace")
+ mock_print.assert_any_call("trying to gather from current context")
+
+ def test_get_current_namespace_incluster_file_open_error(self):
+ """Test handling of file open errors when reading service account namespace."""
+ with patch("os.path.isfile", return_value=True):
+ with patch(
+ "builtins.open", side_effect=PermissionError("Permission denied")
+ ):
+ with patch("builtins.print") as mock_print:
+ # Mock config_check to avoid kubeconfig fallback
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils.config_check",
+ side_effect=Exception("Config error"),
+ ):
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils._kube_api_error_handling",
+ return_value=None,
+ ):
+ result = get_current_namespace()
+
+ assert result is None
+ # Should see both error messages: in-cluster failure and kubeconfig fallback
+ mock_print.assert_any_call("Unable to find current namespace")
+ mock_print.assert_any_call("trying to gather from current context")
+
+ def test_get_current_namespace_kubeconfig_success(self):
+ """Test successful namespace detection from kubeconfig context."""
+ mock_contexts = [
+ {"name": "context1", "context": {"namespace": "default"}},
+ {"name": "context2", "context": {"namespace": "test-namespace"}},
+ ]
+ mock_active_context = {
+ "name": "context2",
+ "context": {"namespace": "test-namespace"},
+ }
+
+ with patch("os.path.isfile", return_value=False):
+ with patch("builtins.print") as mock_print:
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils.config_check",
+ return_value="~/.kube/config",
+ ):
+ with patch(
+ "kubernetes.config.list_kube_config_contexts",
+ return_value=(mock_contexts, mock_active_context),
+ ):
+ result = get_current_namespace()
+
+ assert result == "test-namespace"
+ mock_print.assert_called_with("trying to gather from current context")
+
+ def test_get_current_namespace_kubeconfig_no_namespace_in_context(self):
+ """Test handling when kubeconfig context has no namespace field."""
+ mock_contexts = [
+ {"name": "context1", "context": {}},
+ {"name": "context2", "context": {"cluster": "test-cluster"}},
+ ]
+ mock_active_context = {
+ "name": "context2",
+ "context": {"cluster": "test-cluster"},
+ }
+
+ with patch("os.path.isfile", return_value=False):
+ with patch("builtins.print") as mock_print:
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils.config_check",
+ return_value="~/.kube/config",
+ ):
+ with patch(
+ "kubernetes.config.list_kube_config_contexts",
+ return_value=(mock_contexts, mock_active_context),
+ ):
+ result = get_current_namespace()
+
+ assert result is None
+ mock_print.assert_called_with("trying to gather from current context")
+
+ def test_get_current_namespace_kubeconfig_config_check_error(self):
+ """Test handling when config_check raises an exception."""
+ with patch("os.path.isfile", return_value=False):
+ with patch("builtins.print") as mock_print:
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils.config_check",
+ side_effect=Exception("Config error"),
+ ):
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils._kube_api_error_handling",
+ return_value=None,
+ ) as mock_error_handler:
+ result = get_current_namespace()
+
+ assert result is None
+ mock_print.assert_called_with("trying to gather from current context")
+ mock_error_handler.assert_called_once()
+
+ def test_get_current_namespace_kubeconfig_list_contexts_error(self):
+ """Test handling when list_kube_config_contexts raises an exception."""
+ with patch("os.path.isfile", return_value=False):
+ with patch("builtins.print") as mock_print:
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils.config_check",
+ return_value="~/.kube/config",
+ ):
+ with patch(
+ "kubernetes.config.list_kube_config_contexts",
+ side_effect=Exception("Context error"),
+ ):
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils._kube_api_error_handling",
+ return_value=None,
+ ) as mock_error_handler:
+ result = get_current_namespace()
+
+ assert result is None
+ mock_print.assert_called_with("trying to gather from current context")
+ mock_error_handler.assert_called_once()
+
+ def test_get_current_namespace_kubeconfig_key_error(self):
+ """Test handling when accessing context namespace raises KeyError."""
+ mock_contexts = [{"name": "context1", "context": {"namespace": "default"}}]
+ mock_active_context = {"name": "context1"} # Missing 'context' key
+
+ with patch("os.path.isfile", return_value=False):
+ with patch("builtins.print") as mock_print:
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils.config_check",
+ return_value="~/.kube/config",
+ ):
+ with patch(
+ "kubernetes.config.list_kube_config_contexts",
+ return_value=(mock_contexts, mock_active_context),
+ ):
+ result = get_current_namespace()
+
+ assert result is None
+ mock_print.assert_called_with("trying to gather from current context")
+
+ def test_get_current_namespace_fallback_flow(self):
+ """Test the complete fallback flow from in-cluster to kubeconfig."""
+ # First attempt: in-cluster file doesn't exist
+ # Second attempt: kubeconfig context has namespace
+ mock_contexts = [
+ {"name": "context1", "context": {"namespace": "fallback-namespace"}}
+ ]
+ mock_active_context = {
+ "name": "context1",
+ "context": {"namespace": "fallback-namespace"},
+ }
+
+ with patch("os.path.isfile", return_value=False):
+ with patch("builtins.print") as mock_print:
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils.config_check",
+ return_value="~/.kube/config",
+ ):
+ with patch(
+ "kubernetes.config.list_kube_config_contexts",
+ return_value=(mock_contexts, mock_active_context),
+ ):
+ result = get_current_namespace()
+
+ assert result == "fallback-namespace"
+ mock_print.assert_called_with("trying to gather from current context")
+
+ def test_get_current_namespace_complete_failure(self):
+ """Test complete failure scenario where no namespace can be detected."""
+ with patch("os.path.isfile", return_value=False):
+ with patch("builtins.print") as mock_print:
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils.config_check",
+ side_effect=Exception("Config error"),
+ ):
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils._kube_api_error_handling",
+ return_value=None,
+ ):
+ result = get_current_namespace()
+
+ assert result is None
+ mock_print.assert_called_with("trying to gather from current context")
+
+ def test_get_current_namespace_mixed_errors(self):
+ """Test scenario with mixed error conditions."""
+ # In-cluster file exists but read fails, then kubeconfig also fails
+ with patch("os.path.isfile", return_value=True):
+ with patch("builtins.open", side_effect=IOError("File read error")):
+ with patch("builtins.print") as mock_print:
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils.config_check",
+ side_effect=Exception("Config error"),
+ ):
+ with patch(
+ "codeflare_sdk.common.utils.k8s_utils._kube_api_error_handling",
+ return_value=None,
+ ):
+ result = get_current_namespace()
+
+ assert result is None
+ # Should see both error messages
+ assert mock_print.call_count >= 2
diff --git a/src/codeflare_sdk/common/utils/test_validation.py b/src/codeflare_sdk/common/utils/test_validation.py
new file mode 100644
index 00000000..20416d00
--- /dev/null
+++ b/src/codeflare_sdk/common/utils/test_validation.py
@@ -0,0 +1,224 @@
+# Copyright 2022-2025 IBM, Red Hat
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from codeflare_sdk.common.utils.validation import (
+ extract_ray_version_from_image,
+ validate_ray_version_compatibility,
+)
+from codeflare_sdk.common.utils.constants import RAY_VERSION
+
+
+class TestRayVersionDetection:
+ """Test Ray version detection from container image names."""
+
+ def test_extract_ray_version_standard_format(self):
+ """Test extraction from standard Ray image formats."""
+ # Standard format
+ assert extract_ray_version_from_image("ray:2.47.1") == "2.47.1"
+ assert extract_ray_version_from_image("ray:2.46.0") == "2.46.0"
+ assert extract_ray_version_from_image("ray:1.13.0") == "1.13.0"
+
+ def test_extract_ray_version_with_registry(self):
+ """Test extraction from images with registry prefixes."""
+ assert extract_ray_version_from_image("quay.io/ray:2.47.1") == "2.47.1"
+ assert (
+ extract_ray_version_from_image("docker.io/rayproject/ray:2.47.1")
+ == "2.47.1"
+ )
+ assert (
+ extract_ray_version_from_image("gcr.io/my-project/ray:2.47.1") == "2.47.1"
+ )
+
+ def test_extract_ray_version_with_suffixes(self):
+ """Test extraction from images with version suffixes."""
+ assert (
+ extract_ray_version_from_image("quay.io/modh/ray:2.47.1-py311-cu121")
+ == "2.47.1"
+ )
+ assert extract_ray_version_from_image("ray:2.47.1-py311") == "2.47.1"
+ assert extract_ray_version_from_image("ray:2.47.1-gpu") == "2.47.1"
+ assert extract_ray_version_from_image("ray:2.47.1-rocm62") == "2.47.1"
+
+ def test_extract_ray_version_complex_registry_paths(self):
+ """Test extraction from complex registry paths."""
+ assert (
+ extract_ray_version_from_image("quay.io/modh/ray:2.47.1-py311-cu121")
+ == "2.47.1"
+ )
+ assert (
+ extract_ray_version_from_image("registry.company.com/team/ray:2.47.1")
+ == "2.47.1"
+ )
+
+ def test_extract_ray_version_no_version_found(self):
+ """Test cases where no version can be extracted."""
+ # SHA-based tags
+ assert (
+ extract_ray_version_from_image(
+ "quay.io/modh/ray@sha256:6d076aeb38ab3c34a6a2ef0f58dc667089aa15826fa08a73273c629333e12f1e"
+ )
+ is None
+ )
+
+ # Non-semantic versions
+ assert extract_ray_version_from_image("ray:latest") is None
+ assert extract_ray_version_from_image("ray:nightly") is None
+ assert (
+ extract_ray_version_from_image("ray:v2.47") is None
+ ) # Missing patch version
+
+ # Non-Ray images
+ assert extract_ray_version_from_image("python:3.11") is None
+ assert extract_ray_version_from_image("ubuntu:20.04") is None
+
+ # Empty or None
+ assert extract_ray_version_from_image("") is None
+ assert extract_ray_version_from_image(None) is None
+
+ def test_extract_ray_version_edge_cases(self):
+ """Test edge cases for version extraction."""
+ # Version with 'v' prefix should not match our pattern
+ assert extract_ray_version_from_image("ray:v2.47.1") is None
+
+ # Multiple version-like patterns - should match the first valid one
+ assert (
+ extract_ray_version_from_image("registry/ray:2.47.1-based-on-1.0.0")
+ == "2.47.1"
+ )
+
+
+class TestRayVersionValidation:
+ """Test Ray version compatibility validation."""
+
+ def test_validate_compatible_versions(self):
+ """Test validation with compatible Ray versions."""
+ # Exact match
+ is_compatible, is_warning, message = validate_ray_version_compatibility(
+ f"ray:{RAY_VERSION}"
+ )
+ assert is_compatible is True
+ assert is_warning is False
+ assert "Ray versions match" in message
+
+ # With registry and suffixes
+ is_compatible, is_warning, message = validate_ray_version_compatibility(
+ f"quay.io/modh/ray:{RAY_VERSION}-py311-cu121"
+ )
+ assert is_compatible is True
+ assert is_warning is False
+ assert "Ray versions match" in message
+
+ def test_validate_incompatible_versions(self):
+ """Test validation with incompatible Ray versions."""
+ # Different version
+ is_compatible, is_warning, message = validate_ray_version_compatibility(
+ "ray:2.46.0"
+ )
+ assert is_compatible is False
+ assert is_warning is False
+ assert "Ray version mismatch detected" in message
+ assert "CodeFlare SDK uses Ray" in message
+ assert "runtime image uses Ray" in message
+
+ # Older version
+ is_compatible, is_warning, message = validate_ray_version_compatibility(
+ "ray:1.13.0"
+ )
+ assert is_compatible is False
+ assert is_warning is False
+ assert "Ray version mismatch detected" in message
+
+ def test_validate_empty_image(self):
+ """Test validation with no custom image (should use default)."""
+ # Empty string
+ is_compatible, is_warning, message = validate_ray_version_compatibility("")
+ assert is_compatible is True
+ assert is_warning is False
+ assert "Using default Ray image compatible with SDK" in message
+
+ # None
+ is_compatible, is_warning, message = validate_ray_version_compatibility(None)
+ assert is_compatible is True
+ assert is_warning is False
+ assert "Using default Ray image compatible with SDK" in message
+
+ def test_validate_unknown_version(self):
+ """Test validation when version cannot be determined."""
+ # SHA-based image
+ is_compatible, is_warning, message = validate_ray_version_compatibility(
+ "quay.io/modh/ray@sha256:6d076aeb38ab3c34a6a2ef0f58dc667089aa15826fa08a73273c629333e12f1e"
+ )
+ assert is_compatible is True
+ assert is_warning is True
+ assert "Cannot determine Ray version" in message
+
+ # Custom image without version
+ is_compatible, is_warning, message = validate_ray_version_compatibility(
+ "my-custom-ray:latest"
+ )
+ assert is_compatible is True
+ assert is_warning is True
+ assert "Cannot determine Ray version" in message
+
+ def test_validate_custom_sdk_version(self):
+ """Test validation with custom SDK version."""
+ # Compatible with custom SDK version
+ is_compatible, is_warning, message = validate_ray_version_compatibility(
+ "ray:2.46.0", "2.46.0"
+ )
+ assert is_compatible is True
+ assert is_warning is False
+ assert "Ray versions match" in message
+
+ # Incompatible with custom SDK version
+ is_compatible, is_warning, message = validate_ray_version_compatibility(
+ "ray:2.47.1", "2.46.0"
+ )
+ assert is_compatible is False
+ assert is_warning is False
+ assert "CodeFlare SDK uses Ray 2.46.0" in message
+ assert "runtime image uses Ray 2.47.1" in message
+
+ def test_validate_message_content(self):
+ """Test that validation messages contain expected guidance."""
+ # Mismatch message should contain helpful guidance
+ is_compatible, is_warning, message = validate_ray_version_compatibility(
+ "ray:2.46.0"
+ )
+ assert is_compatible is False
+ assert is_warning is False
+ assert "compatibility issues" in message.lower()
+ assert "unexpected behavior" in message.lower()
+ assert "please use a runtime image" in message.lower()
+ assert "update your sdk version" in message.lower()
+
+ def test_semantic_version_comparison(self):
+ """Test that semantic version comparison works correctly."""
+ # Test that 2.10.0 > 2.9.1 (would fail with string comparison)
+ is_compatible, is_warning, message = validate_ray_version_compatibility(
+ "ray:2.10.0", "2.9.1"
+ )
+ assert is_compatible is False
+ assert is_warning is False
+ assert "CodeFlare SDK uses Ray 2.9.1" in message
+ assert "runtime image uses Ray 2.10.0" in message
+
+ # Test that 2.9.1 < 2.10.0 (would fail with string comparison)
+ is_compatible, is_warning, message = validate_ray_version_compatibility(
+ "ray:2.9.1", "2.10.0"
+ )
+ assert is_compatible is False
+ assert is_warning is False
+ assert "CodeFlare SDK uses Ray 2.10.0" in message
+ assert "runtime image uses Ray 2.9.1" in message
diff --git a/src/codeflare_sdk/common/utils/utils.py b/src/codeflare_sdk/common/utils/utils.py
index f876e924..7e30b994 100644
--- a/src/codeflare_sdk/common/utils/utils.py
+++ b/src/codeflare_sdk/common/utils/utils.py
@@ -19,6 +19,17 @@
)
+def update_image(image) -> str:
+ """
+ The update_image() function automatically sets the image config parameter to a preset image based on Python version if not specified.
+ This now points to the centralized function in utils.py.
+ """
+ if not image:
+ # Pull the image based on the matching Python version (or output a warning if not supported)
+ image = get_ray_image_for_python_version(warn_on_unsupported=True)
+ return image
+
+
def get_ray_image_for_python_version(python_version=None, warn_on_unsupported=True):
"""
Get the appropriate Ray image for a given Python version.
diff --git a/src/codeflare_sdk/common/utils/validation.py b/src/codeflare_sdk/common/utils/validation.py
new file mode 100644
index 00000000..ec749f7c
--- /dev/null
+++ b/src/codeflare_sdk/common/utils/validation.py
@@ -0,0 +1,134 @@
+# Copyright 2022-2025 IBM, Red Hat
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Validation utilities for the CodeFlare SDK.
+
+This module contains validation functions used across the SDK for ensuring
+configuration compatibility and correctness.
+"""
+
+import logging
+import re
+from typing import Optional, Tuple
+from packaging.version import Version, InvalidVersion
+from .constants import RAY_VERSION
+
+logger = logging.getLogger(__name__)
+
+
+def extract_ray_version_from_image(image_name: str) -> Optional[str]:
+ """
+ Extract Ray version from a container image name.
+
+ Supports various image naming patterns:
+ - quay.io/modh/ray:2.47.1-py311-cu121
+ - ray:2.47.1
+ - some-registry/ray:2.47.1-py311
+ - quay.io/modh/ray@sha256:... (falls back to None)
+
+ Args:
+ image_name: The container image name/tag
+
+ Returns:
+ The extracted Ray version, or None if not found
+ """
+ if not image_name:
+ return None
+
+ # Pattern to match semantic version after ray: or ray/
+ # Looks for patterns like ray:2.47.1, ray:2.47.1-py311, etc.
+ patterns = [
+ r"ray:(\d+\.\d+\.\d+)", # ray:2.47.1
+ r"ray/[^:]*:(\d+\.\d+\.\d+)", # registry/ray:2.47.1
+ r"/ray:(\d+\.\d+\.\d+)", # any-registry/ray:2.47.1
+ ]
+
+ for pattern in patterns:
+ match = re.search(pattern, image_name)
+ if match:
+ return match.group(1)
+
+ # If we can't extract version, return None to indicate unknown
+ return None
+
+
+def validate_ray_version_compatibility(
+ image_name: str, sdk_ray_version: str = RAY_VERSION
+) -> Tuple[bool, bool, str]:
+ """
+ Validate that the Ray version in the runtime image matches the SDK's Ray version.
+
+ Args:
+ image_name: The container image name/tag
+ sdk_ray_version: The Ray version used by the CodeFlare SDK
+
+ Returns:
+ tuple: (is_compatible, is_warning, message)
+ - is_compatible: True if versions match or cannot be determined, False if mismatch
+ - is_warning: True if this is a warning (non-fatal), False otherwise
+ - message: Descriptive message about the validation result
+ """
+ if not image_name:
+ # No custom image specified, will use default - this is compatible
+ logger.debug("Using default Ray image compatible with SDK")
+ return True, False, "Using default Ray image compatible with SDK"
+
+ image_ray_version = extract_ray_version_from_image(image_name)
+
+ if image_ray_version is None:
+ # Cannot determine version from image name, issue a warning but allow
+ return (
+ True,
+ True,
+ f"Cannot determine Ray version from image '{image_name}'. Please ensure it's compatible with Ray {sdk_ray_version}",
+ )
+
+ # Use semantic version comparison for robust version checking
+ try:
+ sdk_version = Version(sdk_ray_version)
+ image_version = Version(image_ray_version)
+
+ if image_version != sdk_version:
+ # Version mismatch detected
+ message = (
+ f"Ray version mismatch detected!\n"
+ f"CodeFlare SDK uses Ray {sdk_ray_version}, but runtime image uses Ray {image_ray_version}.\n"
+ f"This mismatch can cause compatibility issues and unexpected behavior.\n"
+ f"Please use a runtime image with Ray {sdk_ray_version} or update your SDK version."
+ )
+ return False, False, message
+ except InvalidVersion as e:
+ # If version parsing fails, fall back to string comparison with a warning
+ logger.warning(
+ f"Failed to parse version for comparison ({e}), falling back to string comparison"
+ )
+ if image_ray_version != sdk_ray_version:
+ message = (
+ f"Ray version mismatch detected!\n"
+ f"CodeFlare SDK uses Ray {sdk_ray_version}, but runtime image uses Ray {image_ray_version}.\n"
+ f"This mismatch can cause compatibility issues and unexpected behavior.\n"
+ f"Please use a runtime image with Ray {sdk_ray_version} or update your SDK version."
+ )
+ return False, False, message
+
+ # Versions match
+ logger.debug(
+ f"Ray version validation successful: SDK and runtime image both use Ray {sdk_ray_version}"
+ )
+ return (
+ True,
+ False,
+ f"Ray versions match: SDK and runtime image both use Ray {sdk_ray_version}",
+ )
diff --git a/src/codeflare_sdk/common/widgets/test_widgets.py b/src/codeflare_sdk/common/widgets/test_widgets.py
index f88d8eb2..33beca5c 100644
--- a/src/codeflare_sdk/common/widgets/test_widgets.py
+++ b/src/codeflare_sdk/common/widgets/test_widgets.py
@@ -106,7 +106,7 @@ def test_view_clusters(mocker, capsys):
# Prepare to run view_clusters when notebook environment is detected
mocker.patch("codeflare_sdk.common.widgets.widgets.is_notebook", return_value=True)
mock_get_current_namespace = mocker.patch(
- "codeflare_sdk.ray.cluster.cluster.get_current_namespace",
+ "codeflare_sdk.common.utils.get_current_namespace",
return_value="default",
)
namespace = mock_get_current_namespace.return_value
@@ -250,7 +250,7 @@ def test_ray_cluster_manager_widgets_init(mocker, capsys):
return_value=test_ray_clusters_df,
)
mocker.patch(
- "codeflare_sdk.ray.cluster.cluster.get_current_namespace",
+ "codeflare_sdk.common.utils.get_current_namespace",
return_value=namespace,
)
mock_delete_cluster = mocker.patch(
diff --git a/src/codeflare_sdk/common/widgets/widgets.py b/src/codeflare_sdk/common/widgets/widgets.py
index 36d896e8..91295fa9 100644
--- a/src/codeflare_sdk/common/widgets/widgets.py
+++ b/src/codeflare_sdk/common/widgets/widgets.py
@@ -26,6 +26,8 @@
import ipywidgets as widgets
from IPython.display import display, HTML, Javascript
import pandas as pd
+
+from ...common.utils import get_current_namespace
from ...ray.cluster.config import ClusterConfiguration
from ...ray.cluster.status import RayClusterStatus
from ..kubernetes_cluster import _kube_api_error_handling
@@ -43,8 +45,6 @@ class RayClusterManagerWidgets:
"""
def __init__(self, ray_clusters_df: pd.DataFrame, namespace: str = None):
- from ...ray.cluster.cluster import get_current_namespace
-
# Data
self.ray_clusters_df = ray_clusters_df
self.namespace = get_current_namespace() if not namespace else namespace
@@ -353,7 +353,7 @@ def view_clusters(namespace: str = None):
)
return # Exit function if not in Jupyter Notebook
- from ...ray.cluster.cluster import get_current_namespace
+ from ...common.utils import get_current_namespace
if not namespace:
namespace = get_current_namespace()
diff --git a/src/codeflare_sdk/ray/__init__.py b/src/codeflare_sdk/ray/__init__.py
index ab55cc82..7bd0b2c8 100644
--- a/src/codeflare_sdk/ray/__init__.py
+++ b/src/codeflare_sdk/ray/__init__.py
@@ -4,6 +4,14 @@
RayJobClient,
)
+from .rayjobs import (
+ RayJob,
+ ManagedClusterConfig,
+ RayJobDeploymentStatus,
+ CodeflareRayJobStatus,
+ RayJobInfo,
+)
+
from .cluster import (
Cluster,
ClusterConfiguration,
diff --git a/src/codeflare_sdk/ray/cluster/build_ray_cluster.py b/src/codeflare_sdk/ray/cluster/build_ray_cluster.py
index e8b68919..6a3984b1 100644
--- a/src/codeflare_sdk/ray/cluster/build_ray_cluster.py
+++ b/src/codeflare_sdk/ray/cluster/build_ray_cluster.py
@@ -21,7 +21,7 @@
from ...common.kubernetes_cluster import get_api_client, config_check
from kubernetes.client.exceptions import ApiException
from ...common.utils.constants import RAY_VERSION
-from ...common.utils.utils import get_ray_image_for_python_version
+from ...common.utils.utils import update_image
import codeflare_sdk
import os
@@ -96,9 +96,6 @@
),
]
-# Use centralized mapping from constants (so that we only have to update constants.py)
-SUPPORTED_PYTHON_VERSIONS = constants.SUPPORTED_PYTHON_VERSIONS
-
# RayCluster/AppWrapper builder function
def build_ray_cluster(cluster: "codeflare_sdk.ray.cluster.Cluster"):
@@ -245,6 +242,7 @@ def get_labels(cluster: "codeflare_sdk.ray.cluster.Cluster"):
"""
labels = {
"controller-tools.k8s.io": "1.0",
+ "ray.io/cluster": cluster.config.name, # Enforced label always present
}
if cluster.config.labels != {}:
labels.update(cluster.config.labels)
@@ -269,17 +267,6 @@ def with_nb_annotations(annotations: dict):
# Head/Worker container related functions
-def update_image(image) -> str:
- """
- The update_image() function automatically sets the image config parameter to a preset image based on Python version if not specified.
- This now points to the centralized function in utils.py.
- """
- if not image:
- # Pull the image based on the matching Python version (or output a warning if not supported)
- image = get_ray_image_for_python_version(warn_on_unsupported=True)
- return image
-
-
def get_pod_spec(
cluster: "codeflare_sdk.ray.cluster.Cluster",
containers: List,
diff --git a/src/codeflare_sdk/ray/cluster/cluster.py b/src/codeflare_sdk/ray/cluster/cluster.py
index 4eaa2000..ce078716 100644
--- a/src/codeflare_sdk/ray/cluster/cluster.py
+++ b/src/codeflare_sdk/ray/cluster/cluster.py
@@ -20,8 +20,14 @@
from time import sleep
from typing import List, Optional, Tuple, Dict
+import copy
-from ray.job_submission import JobSubmissionClient
+from ray.job_submission import JobSubmissionClient, JobStatus
+import time
+import uuid
+import warnings
+
+from ...common.utils import get_current_namespace
from ...common.kubernetes_cluster.auth import (
config_check,
@@ -57,7 +63,6 @@
from kubernetes.client.rest import ApiException
from kubernetes.client.rest import ApiException
-import warnings
CF_SDK_FIELD_MANAGER = "codeflare-sdk"
@@ -203,6 +208,10 @@ def apply(self, force=False):
self._throw_for_no_raycluster()
namespace = self.config.namespace
name = self.config.name
+
+ # Regenerate resource_yaml to reflect any configuration changes
+ self.resource_yaml = self.create_resource()
+
try:
self.config_check()
api_instance = client.CustomObjectsApi(get_api_client())
@@ -382,9 +391,14 @@ def is_dashboard_ready(self) -> bool:
bool:
True if the dashboard is ready, False otherwise.
"""
+
+ dashboard_uri = self.cluster_dashboard_uri()
+ if dashboard_uri is None:
+ return False
+
try:
response = requests.get(
- self.cluster_dashboard_uri(),
+ dashboard_uri,
headers=self._client_headers,
timeout=5,
verify=self._client_verify_tls,
@@ -392,6 +406,10 @@ def is_dashboard_ready(self) -> bool:
except requests.exceptions.SSLError: # pragma no cover
# SSL exception occurs when oauth ingress has been created but cluster is not up
return False
+ except Exception: # pragma no cover
+ # Any other exception (connection errors, timeouts, etc.)
+ return False
+
if response.status_code == 200:
return True
else:
@@ -499,6 +517,8 @@ def cluster_dashboard_uri(self) -> str:
):
protocol = "https" if route["spec"].get("tls") else "http"
return f"{protocol}://{route['spec']['host']}"
+ # No route found for this cluster
+ return "Dashboard not available yet, have you run cluster.up()?"
else:
try:
api_instance = client.NetworkingV1Api(get_api_client())
@@ -517,7 +537,8 @@ def cluster_dashboard_uri(self) -> str:
protocol = "http"
elif "route.openshift.io/termination" in annotations:
protocol = "https"
- return f"{protocol}://{ingress.spec.rules[0].host}"
+ return f"{protocol}://{ingress.spec.rules[0].host}"
+
return "Dashboard not available yet, have you run cluster.up()?"
def list_jobs(self) -> List:
@@ -635,32 +656,6 @@ def list_all_queued(
return resources
-def get_current_namespace(): # pragma: no cover
- """
- Retrieves the current Kubernetes namespace.
-
- Returns:
- str:
- The current namespace or None if not found.
- """
- if os.path.isfile("/var/run/secrets/kubernetes.io/serviceaccount/namespace"):
- try:
- file = open("/var/run/secrets/kubernetes.io/serviceaccount/namespace", "r")
- active_context = file.readline().strip("\n")
- return active_context
- except Exception as e:
- print("Unable to find current namespace")
- print("trying to gather from current context")
- try:
- _, active_context = config.list_kube_config_contexts(config_check())
- except Exception as e:
- return _kube_api_error_handling(e)
- try:
- return active_context["context"]["namespace"]
- except KeyError:
- return None
-
-
def get_cluster(
cluster_name: str,
namespace: str = "default",
@@ -760,6 +755,7 @@ def get_cluster(
head_extended_resource_requests=head_extended_resources,
worker_extended_resource_requests=worker_extended_resources,
)
+
# Ignore the warning here for the lack of a ClusterConfiguration
with warnings.catch_warnings():
warnings.filterwarnings(
@@ -803,6 +799,7 @@ def remove_autogenerated_fields(resource):
del resource[key]
else:
remove_autogenerated_fields(resource[key])
+
elif isinstance(resource, list):
for item in resource:
remove_autogenerated_fields(item)
diff --git a/src/codeflare_sdk/ray/cluster/test_build_ray_cluster.py b/src/codeflare_sdk/ray/cluster/test_build_ray_cluster.py
index f970d945..3a7947d3 100644
--- a/src/codeflare_sdk/ray/cluster/test_build_ray_cluster.py
+++ b/src/codeflare_sdk/ray/cluster/test_build_ray_cluster.py
@@ -40,7 +40,7 @@ def test_gen_names_without_name(mocker):
def test_update_image_without_supported_python_version(mocker):
# Mock SUPPORTED_PYTHON_VERSIONS
mocker.patch.dict(
- "codeflare_sdk.ray.cluster.build_ray_cluster.SUPPORTED_PYTHON_VERSIONS",
+ "codeflare_sdk.common.utils.constants.SUPPORTED_PYTHON_VERSIONS",
{
"3.11": "ray-py3.11",
"3.12": "ray-py3.12",
diff --git a/src/codeflare_sdk/ray/cluster/test_cluster.py b/src/codeflare_sdk/ray/cluster/test_cluster.py
index ce684607..9b19e224 100644
--- a/src/codeflare_sdk/ray/cluster/test_cluster.py
+++ b/src/codeflare_sdk/ray/cluster/test_cluster.py
@@ -758,5 +758,11 @@ def custom_side_effect(group, version, namespace, plural, **kwargs):
# Make sure to always keep this function last
def test_cleanup():
- os.remove(f"{aw_dir}test-all-params.yaml")
- os.remove(f"{aw_dir}aw-all-params.yaml")
+ # Remove files only if they exist
+ test_file = f"{aw_dir}test-all-params.yaml"
+ if os.path.exists(test_file):
+ os.remove(test_file)
+
+ aw_file = f"{aw_dir}aw-all-params.yaml"
+ if os.path.exists(aw_file):
+ os.remove(aw_file)
diff --git a/src/codeflare_sdk/ray/rayjobs/__init__.py b/src/codeflare_sdk/ray/rayjobs/__init__.py
new file mode 100644
index 00000000..cd6b4123
--- /dev/null
+++ b/src/codeflare_sdk/ray/rayjobs/__init__.py
@@ -0,0 +1,3 @@
+from .rayjob import RayJob, ManagedClusterConfig
+from .status import RayJobDeploymentStatus, CodeflareRayJobStatus, RayJobInfo
+from .config import ManagedClusterConfig
diff --git a/src/codeflare_sdk/ray/rayjobs/config.py b/src/codeflare_sdk/ray/rayjobs/config.py
new file mode 100644
index 00000000..02ced875
--- /dev/null
+++ b/src/codeflare_sdk/ray/rayjobs/config.py
@@ -0,0 +1,546 @@
+# Copyright 2022 IBM, Red Hat
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+The config sub-module contains the definition of the ManagedClusterConfig dataclass,
+which is used to specify resource requirements and other details when creating a
+Cluster object.
+"""
+
+import pathlib
+from dataclasses import dataclass, field, fields
+from typing import Dict, List, Optional, Union, get_args, get_origin, Any, Tuple
+from kubernetes.client import (
+ V1ConfigMapVolumeSource,
+ V1KeyToPath,
+ V1Toleration,
+ V1Volume,
+ V1VolumeMount,
+ V1ObjectMeta,
+ V1Container,
+ V1ContainerPort,
+ V1Lifecycle,
+ V1ExecAction,
+ V1LifecycleHandler,
+ V1EnvVar,
+ V1PodTemplateSpec,
+ V1PodSpec,
+ V1ResourceRequirements,
+)
+
+import logging
+
+from ...common.utils.constants import MOUNT_PATH, RAY_VERSION
+from ...common.utils.utils import update_image
+
+logger = logging.getLogger(__name__)
+
+dir = pathlib.Path(__file__).parent.parent.resolve()
+
+# https://docs.ray.io/en/latest/ray-core/scheduling/accelerators.html
+DEFAULT_ACCELERATORS = {
+ "nvidia.com/gpu": "GPU",
+ "intel.com/gpu": "GPU",
+ "amd.com/gpu": "GPU",
+ "aws.amazon.com/neuroncore": "neuron_cores",
+ "google.com/tpu": "TPU",
+ "habana.ai/gaudi": "HPU",
+ "huawei.com/Ascend910": "NPU",
+ "huawei.com/Ascend310": "NPU",
+}
+
+# Default volume mounts for CA certificates
+DEFAULT_VOLUME_MOUNTS = [
+ V1VolumeMount(
+ mount_path="/etc/pki/tls/certs/odh-trusted-ca-bundle.crt",
+ name="odh-trusted-ca-cert",
+ sub_path="odh-trusted-ca-bundle.crt",
+ ),
+ V1VolumeMount(
+ mount_path="/etc/ssl/certs/odh-trusted-ca-bundle.crt",
+ name="odh-trusted-ca-cert",
+ sub_path="odh-trusted-ca-bundle.crt",
+ ),
+ V1VolumeMount(
+ mount_path="/etc/pki/tls/certs/odh-ca-bundle.crt",
+ name="odh-ca-cert",
+ sub_path="odh-ca-bundle.crt",
+ ),
+ V1VolumeMount(
+ mount_path="/etc/ssl/certs/odh-ca-bundle.crt",
+ name="odh-ca-cert",
+ sub_path="odh-ca-bundle.crt",
+ ),
+]
+
+# Default volumes for CA certificates
+DEFAULT_VOLUMES = [
+ V1Volume(
+ name="odh-trusted-ca-cert",
+ config_map=V1ConfigMapVolumeSource(
+ name="odh-trusted-ca-bundle",
+ items=[V1KeyToPath(key="ca-bundle.crt", path="odh-trusted-ca-bundle.crt")],
+ optional=True,
+ ),
+ ),
+ V1Volume(
+ name="odh-ca-cert",
+ config_map=V1ConfigMapVolumeSource(
+ name="odh-trusted-ca-bundle",
+ items=[V1KeyToPath(key="odh-ca-bundle.crt", path="odh-ca-bundle.crt")],
+ optional=True,
+ ),
+ ),
+]
+
+
+@dataclass
+class ManagedClusterConfig:
+ """
+ This dataclass is used to specify resource requirements and other details for RayJobs.
+ The cluster name and namespace are automatically derived from the RayJob configuration.
+
+ Args:
+ head_accelerators:
+ A dictionary of extended resource requests for the head node. ex: {"nvidia.com/gpu": 1}
+ head_tolerations:
+ List of tolerations for head nodes.
+ num_workers:
+ The number of workers to create.
+ worker_tolerations:
+ List of tolerations for worker nodes.
+ envs:
+ A dictionary of environment variables to set for the cluster.
+ image:
+ The image to use for the cluster.
+ image_pull_secrets:
+ A list of image pull secrets to use for the cluster.
+ labels:
+ A dictionary of labels to apply to the cluster.
+ worker_accelerators:
+ A dictionary of extended resource requests for each worker. ex: {"nvidia.com/gpu": 1}
+ accelerator_configs:
+ A dictionary of custom resource mappings to map extended resource requests to RayCluster resource names.
+ Defaults to DEFAULT_ACCELERATORS but can be overridden with custom mappings.
+ annotations:
+ A dictionary of annotations to apply to the Job.
+ volumes:
+ A list of V1Volume objects to add to the Cluster
+ volume_mounts:
+ A list of V1VolumeMount objects to add to the Cluster
+ """
+
+ head_cpu_requests: Union[int, str] = 2
+ head_cpu_limits: Union[int, str] = 2
+ head_memory_requests: Union[int, str] = 8
+ head_memory_limits: Union[int, str] = 8
+ head_accelerators: Dict[str, Union[str, int]] = field(default_factory=dict)
+ head_tolerations: Optional[List[V1Toleration]] = None
+ worker_cpu_requests: Union[int, str] = 1
+ worker_cpu_limits: Union[int, str] = 1
+ num_workers: int = 1
+ worker_memory_requests: Union[int, str] = 2
+ worker_memory_limits: Union[int, str] = 2
+ worker_tolerations: Optional[List[V1Toleration]] = None
+ envs: Dict[str, str] = field(default_factory=dict)
+ image: str = ""
+ image_pull_secrets: List[str] = field(default_factory=list)
+ labels: Dict[str, str] = field(default_factory=dict)
+ worker_accelerators: Dict[str, Union[str, int]] = field(default_factory=dict)
+ accelerator_configs: Dict[str, str] = field(
+ default_factory=lambda: DEFAULT_ACCELERATORS.copy()
+ )
+ annotations: Dict[str, str] = field(default_factory=dict)
+ volumes: list[V1Volume] = field(default_factory=list)
+ volume_mounts: list[V1VolumeMount] = field(default_factory=list)
+
+ def __post_init__(self):
+ self.envs["RAY_USAGE_STATS_ENABLED"] = "0"
+
+ self._validate_types()
+ self._memory_to_string()
+ self._validate_gpu_config(self.head_accelerators)
+ self._validate_gpu_config(self.worker_accelerators)
+
+ def _validate_gpu_config(self, gpu_config: Dict[str, int]):
+ for k in gpu_config.keys():
+ if k not in self.accelerator_configs.keys():
+ raise ValueError(
+ f"GPU configuration '{k}' not found in accelerator_configs, available resources are {list(self.accelerator_configs.keys())}, to add more supported resources use accelerator_configs. i.e. accelerator_configs = {{'{k}': 'FOO_BAR'}}"
+ )
+
+ def _memory_to_string(self):
+ if isinstance(self.head_memory_requests, int):
+ self.head_memory_requests = f"{self.head_memory_requests}G"
+ if isinstance(self.head_memory_limits, int):
+ self.head_memory_limits = f"{self.head_memory_limits}G"
+ if isinstance(self.worker_memory_requests, int):
+ self.worker_memory_requests = f"{self.worker_memory_requests}G"
+ if isinstance(self.worker_memory_limits, int):
+ self.worker_memory_limits = f"{self.worker_memory_limits}G"
+
+ def _validate_types(self):
+ """Validate the types of all fields in the ManagedClusterConfig dataclass."""
+ errors = []
+ for field_info in fields(self):
+ value = getattr(self, field_info.name)
+ expected_type = field_info.type
+ if not self._is_type(value, expected_type):
+ errors.append(f"'{field_info.name}' should be of type {expected_type}.")
+
+ if errors:
+ raise TypeError("Type validation failed:\n" + "\n".join(errors))
+
+ @staticmethod
+ def _is_type(value, expected_type):
+ """Check if the value matches the expected type."""
+
+ def check_type(value, expected_type):
+ origin_type = get_origin(expected_type)
+ args = get_args(expected_type)
+ if origin_type is Union:
+ return any(check_type(value, union_type) for union_type in args)
+ if origin_type is list:
+ if value is not None:
+ return all(check_type(elem, args[0]) for elem in (value or []))
+ else:
+ return True
+ if origin_type is dict:
+ if value is not None:
+ return all(
+ check_type(k, args[0]) and check_type(v, args[1])
+ for k, v in value.items()
+ )
+ else:
+ return True
+ if origin_type is tuple:
+ return all(check_type(elem, etype) for elem, etype in zip(value, args))
+ if expected_type is int:
+ return isinstance(value, int) and not isinstance(value, bool)
+ if expected_type is bool:
+ return isinstance(value, bool)
+ return isinstance(value, expected_type)
+
+ return check_type(value, expected_type)
+
+ def build_ray_cluster_spec(self, cluster_name: str) -> Dict[str, Any]:
+ """
+ Build the RayCluster spec from ManagedClusterConfig for embedding in RayJob.
+
+ Args:
+ self: The cluster configuration object (ManagedClusterConfig)
+ cluster_name: The name for the cluster (derived from RayJob name)
+
+ Returns:
+ Dict containing the RayCluster spec for embedding in RayJob
+ """
+ ray_cluster_spec = {
+ "rayVersion": RAY_VERSION,
+ "headGroupSpec": self._build_head_group_spec(),
+ "workerGroupSpecs": [self._build_worker_group_spec(cluster_name)],
+ }
+
+ return ray_cluster_spec
+
+ def _build_head_group_spec(self) -> Dict[str, Any]:
+ """Build the head group specification."""
+ return {
+ "serviceType": "ClusterIP",
+ "enableIngress": False,
+ "rayStartParams": self._build_head_ray_params(),
+ "template": V1PodTemplateSpec(
+ metadata=V1ObjectMeta(annotations=self.annotations),
+ spec=self._build_pod_spec(self._build_head_container(), is_head=True),
+ ),
+ }
+
+ def _build_worker_group_spec(self, cluster_name: str) -> Dict[str, Any]:
+ """Build the worker group specification."""
+ return {
+ "replicas": self.num_workers,
+ "minReplicas": self.num_workers,
+ "maxReplicas": self.num_workers,
+ "groupName": f"worker-group-{cluster_name}",
+ "rayStartParams": self._build_worker_ray_params(),
+ "template": V1PodTemplateSpec(
+ metadata=V1ObjectMeta(annotations=self.annotations),
+ spec=self._build_pod_spec(
+ self._build_worker_container(),
+ is_head=False,
+ ),
+ ),
+ }
+
+ def _build_head_ray_params(self) -> Dict[str, str]:
+ """Build Ray start parameters for head node."""
+ params = {
+ "dashboard-host": "0.0.0.0",
+ "block": "true",
+ }
+
+ # Add GPU count if specified
+ if self.head_accelerators:
+ gpu_count = sum(
+ count
+ for resource_type, count in self.head_accelerators.items()
+ if "gpu" in resource_type.lower()
+ )
+ if gpu_count > 0:
+ params["num-gpus"] = str(gpu_count)
+
+ return params
+
+ def _build_worker_ray_params(self) -> Dict[str, str]:
+ """Build Ray start parameters for worker nodes."""
+ params = {
+ "block": "true",
+ }
+
+ # Add GPU count if specified
+ if self.worker_accelerators:
+ gpu_count = sum(
+ count
+ for resource_type, count in self.worker_accelerators.items()
+ if "gpu" in resource_type.lower()
+ )
+ if gpu_count > 0:
+ params["num-gpus"] = str(gpu_count)
+
+ return params
+
+ def _build_head_container(self) -> V1Container:
+ """Build the head container specification."""
+ container = V1Container(
+ name="ray-head",
+ image=update_image(self.image),
+ image_pull_policy="IfNotPresent", # Always IfNotPresent for RayJobs
+ ports=[
+ V1ContainerPort(name="gcs", container_port=6379),
+ V1ContainerPort(name="dashboard", container_port=8265),
+ V1ContainerPort(name="client", container_port=10001),
+ ],
+ lifecycle=V1Lifecycle(
+ pre_stop=V1LifecycleHandler(
+ _exec=V1ExecAction(command=["/bin/sh", "-c", "ray stop"])
+ )
+ ),
+ resources=self._build_resource_requirements(
+ self.head_cpu_requests,
+ self.head_cpu_limits,
+ self.head_memory_requests,
+ self.head_memory_limits,
+ self.head_accelerators,
+ ),
+ volume_mounts=self._generate_volume_mounts(),
+ env=self._build_env_vars() if hasattr(self, "envs") and self.envs else None,
+ )
+
+ return container
+
+ def _build_worker_container(self) -> V1Container:
+ """Build the worker container specification."""
+ container = V1Container(
+ name="ray-worker",
+ image=update_image(self.image),
+ image_pull_policy="IfNotPresent", # Always IfNotPresent for RayJobs
+ lifecycle=V1Lifecycle(
+ pre_stop=V1LifecycleHandler(
+ _exec=V1ExecAction(command=["/bin/sh", "-c", "ray stop"])
+ )
+ ),
+ resources=self._build_resource_requirements(
+ self.worker_cpu_requests,
+ self.worker_cpu_limits,
+ self.worker_memory_requests,
+ self.worker_memory_limits,
+ self.worker_accelerators,
+ ),
+ volume_mounts=self._generate_volume_mounts(),
+ env=self._build_env_vars() if hasattr(self, "envs") and self.envs else None,
+ )
+
+ return container
+
+ def _build_resource_requirements(
+ self,
+ cpu_requests: Union[int, str],
+ cpu_limits: Union[int, str],
+ memory_requests: Union[int, str],
+ memory_limits: Union[int, str],
+ extended_resource_requests: Dict[str, Union[int, str]] = None,
+ ) -> V1ResourceRequirements:
+ """Build Kubernetes resource requirements."""
+ resource_requirements = V1ResourceRequirements(
+ requests={"cpu": cpu_requests, "memory": memory_requests},
+ limits={"cpu": cpu_limits, "memory": memory_limits},
+ )
+
+ # Add extended resources (e.g., GPUs)
+ if extended_resource_requests:
+ for resource_type, amount in extended_resource_requests.items():
+ resource_requirements.limits[resource_type] = amount
+ resource_requirements.requests[resource_type] = amount
+
+ return resource_requirements
+
+ def _build_pod_spec(self, container: V1Container, is_head: bool) -> V1PodSpec:
+ """Build the pod specification."""
+ pod_spec = V1PodSpec(
+ containers=[container],
+ volumes=self._generate_volumes(),
+ restart_policy="Never", # RayJobs should not restart
+ )
+
+ # Add tolerations if specified
+ if is_head and hasattr(self, "head_tolerations") and self.head_tolerations:
+ pod_spec.tolerations = self.head_tolerations
+ elif (
+ not is_head
+ and hasattr(self, "worker_tolerations")
+ and self.worker_tolerations
+ ):
+ pod_spec.tolerations = self.worker_tolerations
+
+ # Add image pull secrets if specified
+ if hasattr(self, "image_pull_secrets") and self.image_pull_secrets:
+ from kubernetes.client import V1LocalObjectReference
+
+ pod_spec.image_pull_secrets = [
+ V1LocalObjectReference(name=secret)
+ for secret in self.image_pull_secrets
+ ]
+
+ return pod_spec
+
+ def _generate_volume_mounts(self) -> list:
+ """Generate volume mounts for the container."""
+ volume_mounts = DEFAULT_VOLUME_MOUNTS.copy()
+
+ # Add custom volume mounts if specified
+ if hasattr(self, "volume_mounts") and self.volume_mounts:
+ volume_mounts.extend(self.volume_mounts)
+
+ return volume_mounts
+
+ def _generate_volumes(self) -> list:
+ """Generate volumes for the pod."""
+ volumes = DEFAULT_VOLUMES.copy()
+
+ # Add custom volumes if specified
+ if hasattr(self, "volumes") and self.volumes:
+ volumes.extend(self.volumes)
+
+ return volumes
+
+ def _build_env_vars(self) -> list:
+ """Build environment variables list."""
+ return [V1EnvVar(name=key, value=value) for key, value in self.envs.items()]
+
+ def add_script_volumes(self, configmap_name: str, mount_path: str = MOUNT_PATH):
+ """
+ Add script volume and mount references to cluster configuration.
+
+ Args:
+ configmap_name: Name of the ConfigMap containing scripts
+ mount_path: Where to mount scripts in containers (default: /home/ray/scripts)
+ """
+ # Check if script volume already exists
+ volume_name = "ray-job-scripts"
+ existing_volume = next(
+ (v for v in self.volumes if getattr(v, "name", None) == volume_name), None
+ )
+ if existing_volume:
+ logger.debug(f"Script volume '{volume_name}' already exists, skipping...")
+ return
+
+ # Check if script mount already exists
+ existing_mount = next(
+ (m for m in self.volume_mounts if getattr(m, "name", None) == volume_name),
+ None,
+ )
+ if existing_mount:
+ logger.debug(
+ f"Script volume mount '{volume_name}' already exists, skipping..."
+ )
+ return
+
+ # Add script volume to cluster configuration
+ script_volume = V1Volume(
+ name=volume_name, config_map=V1ConfigMapVolumeSource(name=configmap_name)
+ )
+ self.volumes.append(script_volume)
+
+ # Add script volume mount to cluster configuration
+ script_mount = V1VolumeMount(name=volume_name, mount_path=mount_path)
+ self.volume_mounts.append(script_mount)
+
+ logger.info(
+ f"Added script volume '{configmap_name}' to cluster config: mount_path={mount_path}"
+ )
+
+ def validate_configmap_size(self, scripts: Dict[str, str]) -> None:
+ total_size = sum(len(content.encode("utf-8")) for content in scripts.values())
+ if total_size > 1024 * 1024: # 1MB
+ raise ValueError(
+ f"ConfigMap size exceeds 1MB limit. Total size: {total_size} bytes"
+ )
+
+ def build_script_configmap_spec(
+ self, job_name: str, namespace: str, scripts: Dict[str, str]
+ ) -> Dict[str, Any]:
+ """
+ Build ConfigMap specification for scripts
+
+ Args:
+ job_name: Name of the RayJob (used for ConfigMap naming)
+ namespace: Kubernetes namespace
+ scripts: Dictionary of script_name -> script_content
+
+ Returns:
+ Dict: ConfigMap specification ready for Kubernetes API
+ """
+ configmap_name = f"{job_name}-scripts"
+ return {
+ "apiVersion": "v1",
+ "kind": "ConfigMap",
+ "metadata": {
+ "name": configmap_name,
+ "namespace": namespace,
+ "labels": {
+ "ray.io/job-name": job_name,
+ "app.kubernetes.io/managed-by": "codeflare-sdk",
+ "app.kubernetes.io/component": "rayjob-scripts",
+ },
+ },
+ "data": scripts,
+ }
+
+ def build_script_volume_specs(
+ self, configmap_name: str, mount_path: str = MOUNT_PATH
+ ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+ """
+ Build volume and mount specifications for scripts
+
+ Args:
+ configmap_name: Name of the ConfigMap containing scripts
+ mount_path: Where to mount scripts in containers
+
+ Returns:
+ Tuple of (volume_spec, mount_spec) as dictionaries
+ """
+ volume_spec = {"name": "ray-job-scripts", "configMap": {"name": configmap_name}}
+
+ mount_spec = {"name": "ray-job-scripts", "mountPath": mount_path}
+
+ return volume_spec, mount_spec
diff --git a/src/codeflare_sdk/ray/rayjobs/pretty_print.py b/src/codeflare_sdk/ray/rayjobs/pretty_print.py
new file mode 100644
index 00000000..34e8dfa1
--- /dev/null
+++ b/src/codeflare_sdk/ray/rayjobs/pretty_print.py
@@ -0,0 +1,117 @@
+# Copyright 2025 IBM, Red Hat
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This sub-module exists primarily to be used internally by the RayJob object
+(in the rayjob sub-module) for pretty-printing job status and details.
+"""
+
+from rich.console import Console
+from rich.table import Table
+from rich.panel import Panel
+from typing import Tuple, Optional
+
+from .status import RayJobDeploymentStatus, RayJobInfo
+
+
+def print_job_status(job_info: RayJobInfo):
+ """
+ Pretty print the job status in a format similar to cluster status.
+ """
+ status_display, header_color = _get_status_display(job_info.status)
+
+ # Create main info table
+ table = _create_info_table(header_color, job_info.name, status_display)
+ table.add_row(f"[bold]Job ID:[/bold] {job_info.job_id}")
+ table.add_row(f"[bold]Status:[/bold] {job_info.status.value}")
+ table.add_row(f"[bold]RayCluster:[/bold] {job_info.cluster_name}")
+ table.add_row(f"[bold]Namespace:[/bold] {job_info.namespace}")
+
+ # Add timing information if available
+ if job_info.start_time:
+ table.add_row()
+ table.add_row(f"[bold]Started:[/bold] {job_info.start_time}")
+
+ # Add attempt counts if there are failures
+ if job_info.failed_attempts > 0:
+ table.add_row(f"[bold]Failed Attempts:[/bold] {job_info.failed_attempts}")
+
+ _print_table_in_panel(table)
+
+
+def print_no_job_found(job_name: str, namespace: str):
+ """
+ Print a message when no job is found.
+ """
+ # Create table with error message
+ table = _create_info_table(
+ "[white on red][bold]Name", job_name, "[bold red]No RayJob found"
+ )
+ table.add_row()
+ table.add_row("Please run rayjob.submit() to submit a job.")
+ table.add_row()
+ table.add_row(f"[bold]Namespace:[/bold] {namespace}")
+
+ _print_table_in_panel(table)
+
+
+def _get_status_display(status: RayJobDeploymentStatus) -> Tuple[str, str]:
+ """
+ Get the display string and header color for a given status.
+
+ Returns:
+ Tuple of (status_display, header_color)
+ """
+ status_mapping = {
+ RayJobDeploymentStatus.COMPLETE: (
+ "Complete :white_heavy_check_mark:",
+ "[white on green][bold]Name",
+ ),
+ RayJobDeploymentStatus.RUNNING: ("Running :gear:", "[white on blue][bold]Name"),
+ RayJobDeploymentStatus.FAILED: ("Failed :x:", "[white on red][bold]Name"),
+ RayJobDeploymentStatus.SUSPENDED: (
+ "Suspended :pause_button:",
+ "[white on yellow][bold]Name",
+ ),
+ }
+
+ return status_mapping.get(
+ status, ("Unknown :question:", "[white on red][bold]Name")
+ )
+
+
+def _create_info_table(header_color: str, name: str, status_display: str) -> Table:
+ """
+ Create a standardized info table with header and status.
+
+ Returns:
+ Table with header row, name/status row, and empty separator row
+ """
+ table = Table(box=None, show_header=False)
+ table.add_row(header_color)
+ table.add_row("[bold underline]" + name, status_display)
+ table.add_row() # Empty separator row
+ return table
+
+
+def _print_table_in_panel(table: Table):
+ """
+ Print a table wrapped in a consistent panel format.
+ """
+ console = Console()
+ main_table = Table(
+ box=None, title="[bold] :package: CodeFlare RayJob Status :package:"
+ )
+ main_table.add_row(Panel.fit(table))
+ console.print(main_table)
diff --git a/src/codeflare_sdk/ray/rayjobs/rayjob.py b/src/codeflare_sdk/ray/rayjobs/rayjob.py
new file mode 100644
index 00000000..228f9bb0
--- /dev/null
+++ b/src/codeflare_sdk/ray/rayjobs/rayjob.py
@@ -0,0 +1,674 @@
+# Copyright 2022-2025 IBM, Red Hat
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+RayJob client for submitting and managing Ray jobs using the kuberay python client.
+"""
+
+import logging
+import warnings
+import os
+import re
+import ast
+from typing import Dict, Any, Optional, Tuple
+from codeflare_sdk.common.kueue.kueue import get_default_kueue_name
+from codeflare_sdk.common.utils.constants import MOUNT_PATH
+from kubernetes import client
+from ...common.kubernetes_cluster.auth import get_api_client
+from python_client.kuberay_job_api import RayjobApi
+from python_client.kuberay_cluster_api import RayClusterApi
+from codeflare_sdk.ray.rayjobs.config import ManagedClusterConfig
+
+from ...common.utils import get_current_namespace
+from ...common.utils.validation import validate_ray_version_compatibility
+
+from .status import (
+ RayJobDeploymentStatus,
+ CodeflareRayJobStatus,
+ RayJobInfo,
+)
+from . import pretty_print
+
+
+logger = logging.getLogger(__name__)
+
+
+class RayJob:
+ """
+ A client for managing Ray jobs using the KubeRay operator.
+
+ This class provides a simplified interface for submitting and managing
+ RayJob CRs (using the KubeRay RayJob python client).
+ """
+
+ def __init__(
+ self,
+ job_name: str,
+ entrypoint: str,
+ cluster_name: Optional[str] = None,
+ cluster_config: Optional[ManagedClusterConfig] = None,
+ namespace: Optional[str] = None,
+ runtime_env: Optional[Dict[str, Any]] = None,
+ ttl_seconds_after_finished: int = 0,
+ active_deadline_seconds: Optional[int] = None,
+ local_queue: Optional[str] = None,
+ ):
+ """
+ Initialize a RayJob instance.
+
+ Args:
+ job_name: The name for the Ray job
+ entrypoint: The Python script or command to run (required)
+ cluster_name: The name of an existing Ray cluster (optional if cluster_config provided)
+ cluster_config: Configuration for creating a new cluster (optional if cluster_name provided)
+ namespace: The Kubernetes namespace (auto-detected if not specified)
+ runtime_env: Ray runtime environment configuration (optional)
+ ttl_seconds_after_finished: Seconds to wait before cleanup after job finishes (default: 0)
+ active_deadline_seconds: Maximum time the job can run before being terminated (optional)
+ local_queue: The Kueue LocalQueue to submit the job to (optional)
+
+ Note:
+ - True if cluster_config is provided (new cluster will be cleaned up)
+ - False if cluster_name is provided (existing cluster will not be shut down)
+ - User can explicitly set this value to override auto-detection
+ """
+ if cluster_name is None and cluster_config is None:
+ raise ValueError(
+ "❌ Configuration Error: You must provide either 'cluster_name' (for existing cluster) "
+ "or 'cluster_config' (to create new cluster), but not both."
+ )
+
+ if cluster_name is not None and cluster_config is not None:
+ raise ValueError(
+ "❌ Configuration Error: You cannot specify both 'cluster_name' and 'cluster_config'. "
+ "Choose one approach:\n"
+ "• Use 'cluster_name' to connect to an existing cluster\n"
+ "• Use 'cluster_config' to create a new cluster"
+ )
+
+ if cluster_config is None and cluster_name is None:
+ raise ValueError(
+ "❌ Configuration Error: When not providing 'cluster_config', 'cluster_name' is required "
+ "to specify which existing cluster to use."
+ )
+
+ self.name = job_name
+ self.entrypoint = entrypoint
+ self.runtime_env = runtime_env
+ self.ttl_seconds_after_finished = ttl_seconds_after_finished
+ self.active_deadline_seconds = active_deadline_seconds
+ self.local_queue = local_queue
+
+ if namespace is None:
+ detected_namespace = get_current_namespace()
+ if detected_namespace:
+ self.namespace = detected_namespace
+ logger.info(f"Auto-detected namespace: {self.namespace}")
+ else:
+ raise ValueError(
+ "❌ Configuration Error: Could not auto-detect Kubernetes namespace. "
+ "Please explicitly specify the 'namespace' parameter. "
+ )
+ else:
+ self.namespace = namespace
+
+ self._cluster_name = cluster_name
+ self._cluster_config = cluster_config
+
+ if cluster_config is not None:
+ self.cluster_name = f"{job_name}-cluster"
+ logger.info(f"Creating new cluster: {self.cluster_name}")
+ else:
+ # Using existing cluster: cluster_name must be provided
+ if cluster_name is None:
+ raise ValueError(
+ "❌ Configuration Error: a 'cluster_name' is required when not providing 'cluster_config'"
+ )
+ self.cluster_name = cluster_name
+ logger.info(f"Using existing cluster: {self.cluster_name}")
+
+ self._api = RayjobApi()
+ self._cluster_api = RayClusterApi()
+
+ logger.info(f"Initialized RayJob: {self.name} in namespace: {self.namespace}")
+
+ def submit(self) -> str:
+ if not self.entrypoint:
+ raise ValueError("Entrypoint must be provided to submit a RayJob")
+
+ self._validate_ray_version_compatibility()
+
+ rayjob_cr = self._build_rayjob_cr()
+
+ logger.info(f"Submitting RayJob {self.name} to Kuberay operator")
+ result = self._api.submit_job(k8s_namespace=self.namespace, job=rayjob_cr)
+
+ if result:
+ logger.info(f"Successfully submitted RayJob {self.name}")
+
+ # Handle script files after RayJob creation so we can set owner reference
+ if self._cluster_config is not None:
+ scripts = self._extract_script_files_from_entrypoint()
+ if scripts:
+ self._handle_script_volumes_for_new_cluster(scripts, result)
+ elif self._cluster_name:
+ scripts = self._extract_script_files_from_entrypoint()
+ if scripts:
+ self._handle_script_volumes_for_existing_cluster(scripts, result)
+
+ return self.name
+ else:
+ raise RuntimeError(f"Failed to submit RayJob {self.name}")
+
+ def stop(self):
+ """
+ Suspend the Ray job.
+ """
+ stopped = self._api.suspend_job(name=self.name, k8s_namespace=self.namespace)
+ if stopped:
+ logger.info(f"Successfully stopped the RayJob {self.name}")
+ return True
+ else:
+ raise RuntimeError(f"Failed to stop the RayJob {self.name}")
+
+ def resubmit(self):
+ """
+ Resubmit the Ray job.
+ """
+ if self._api.resubmit_job(name=self.name, k8s_namespace=self.namespace):
+ logger.info(f"Successfully resubmitted the RayJob {self.name}")
+ return True
+ else:
+ raise RuntimeError(f"Failed to resubmit the RayJob {self.name}")
+
+ def delete(self):
+ """
+ Delete the Ray job.
+ """
+ deleted = self._api.delete_job(name=self.name, k8s_namespace=self.namespace)
+ if deleted:
+ logger.info(f"Successfully deleted the RayJob {self.name}")
+ return True
+ else:
+ raise RuntimeError(f"Failed to delete the RayJob {self.name}")
+
+ def _build_rayjob_cr(self) -> Dict[str, Any]:
+ """
+ Build the RayJob custom resource specification using native RayJob capabilities.
+ """
+ rayjob_cr = {
+ "apiVersion": "ray.io/v1",
+ "kind": "RayJob",
+ "metadata": {
+ "name": self.name,
+ "namespace": self.namespace,
+ },
+ "spec": {
+ "entrypoint": self.entrypoint,
+ "ttlSecondsAfterFinished": self.ttl_seconds_after_finished,
+ "shutdownAfterJobFinishes": self._cluster_config is not None,
+ },
+ }
+
+ labels = {}
+ # If cluster_config is provided, use the local_queue from the cluster_config
+ if self._cluster_config is not None:
+ if self.local_queue:
+ labels["kueue.x-k8s.io/queue-name"] = self.local_queue
+ else:
+ default_queue = get_default_kueue_name(self.namespace)
+ if default_queue:
+ labels["kueue.x-k8s.io/queue-name"] = default_queue
+ else:
+ # No default queue found, use "default" as fallback
+ labels["kueue.x-k8s.io/queue-name"] = "default"
+ logger.warning(
+ f"No default Kueue LocalQueue found in namespace '{self.namespace}'. "
+ f"Using 'default' as the queue name. If a LocalQueue named 'default' "
+ f"does not exist, the RayJob submission will fail. "
+ f"To fix this, please explicitly specify the 'local_queue' parameter."
+ )
+
+ rayjob_cr["metadata"]["labels"] = labels
+
+ # When using Kueue (queue label present), start with suspend=true
+ # Kueue will unsuspend the job once the workload is admitted
+ if labels.get("kueue.x-k8s.io/queue-name"):
+ rayjob_cr["spec"]["suspend"] = True
+
+ # Add active deadline if specified
+ if self.active_deadline_seconds:
+ rayjob_cr["spec"]["activeDeadlineSeconds"] = self.active_deadline_seconds
+
+ # Add runtime environment if specified
+ if self.runtime_env:
+ rayjob_cr["spec"]["runtimeEnvYAML"] = str(self.runtime_env)
+
+ # Configure cluster: either use existing or create new
+ if self._cluster_config is not None:
+ ray_cluster_spec = self._cluster_config.build_ray_cluster_spec(
+ cluster_name=self.cluster_name
+ )
+
+ logger.info(
+ f"Built RayCluster spec using RayJob-specific builder for cluster: {self.cluster_name}"
+ )
+
+ rayjob_cr["spec"]["rayClusterSpec"] = ray_cluster_spec
+
+ logger.info(f"RayJob will create new cluster: {self.cluster_name}")
+ else:
+ # Use clusterSelector to reference existing cluster
+ rayjob_cr["spec"]["clusterSelector"] = {"ray.io/cluster": self.cluster_name}
+ logger.info(f"RayJob will use existing cluster: {self.cluster_name}")
+
+ return rayjob_cr
+
+ def _validate_ray_version_compatibility(self):
+ """
+ Validate Ray version compatibility for cluster_config image.
+ Raises ValueError if there is a version mismatch.
+ """
+ # Validate cluster_config image if creating new cluster
+ if self._cluster_config is not None:
+ self._validate_cluster_config_image()
+
+ def _validate_cluster_config_image(self):
+ """
+ Validate that the Ray version in cluster_config image matches the SDK's Ray version.
+ """
+ if not hasattr(self._cluster_config, "image"):
+ logger.debug(
+ "No image attribute found in cluster config, skipping validation"
+ )
+ return
+
+ image = self._cluster_config.image
+ if not image:
+ logger.debug("Cluster config image is empty, skipping validation")
+ return
+
+ if not isinstance(image, str):
+ logger.warning(
+ f"Cluster config image should be a string, got {type(image).__name__}: {image}"
+ )
+ return # Skip validation for malformed image
+
+ is_compatible, is_warning, message = validate_ray_version_compatibility(image)
+ if not is_compatible:
+ raise ValueError(f"Cluster config image: {message}")
+ elif is_warning:
+ warnings.warn(f"Cluster config image: {message}")
+
+ def status(
+ self, print_to_console: bool = True
+ ) -> Tuple[CodeflareRayJobStatus, bool]:
+ """
+ Get the status of the Ray job.
+
+ Args:
+ print_to_console (bool): Whether to print formatted status to console (default: True)
+
+ Returns:
+ Tuple of (CodeflareRayJobStatus, ready: bool) where ready indicates job completion
+ """
+ status_data = self._api.get_job_status(
+ name=self.name, k8s_namespace=self.namespace
+ )
+
+ if not status_data:
+ if print_to_console:
+ pretty_print.print_no_job_found(self.name, self.namespace)
+ return CodeflareRayJobStatus.UNKNOWN, False
+
+ # Map deployment status to our enums
+ deployment_status_str = status_data.get("jobDeploymentStatus", "Unknown")
+
+ try:
+ deployment_status = RayJobDeploymentStatus(deployment_status_str)
+ except ValueError:
+ deployment_status = RayJobDeploymentStatus.UNKNOWN
+
+ # Create RayJobInfo dataclass
+ job_info = RayJobInfo(
+ name=self.name,
+ job_id=status_data.get("jobId", ""),
+ status=deployment_status,
+ namespace=self.namespace,
+ cluster_name=self.cluster_name,
+ start_time=status_data.get("startTime"),
+ end_time=status_data.get("endTime"),
+ failed_attempts=status_data.get("failed", 0),
+ succeeded_attempts=status_data.get("succeeded", 0),
+ )
+
+ # Map to CodeFlare status and determine readiness
+ codeflare_status, ready = self._map_to_codeflare_status(deployment_status)
+
+ if print_to_console:
+ pretty_print.print_job_status(job_info)
+
+ return codeflare_status, ready
+
+ def _map_to_codeflare_status(
+ self, deployment_status: RayJobDeploymentStatus
+ ) -> Tuple[CodeflareRayJobStatus, bool]:
+ """
+ Map deployment status to CodeFlare status and determine readiness.
+
+ Returns:
+ Tuple of (CodeflareRayJobStatus, ready: bool)
+ """
+ status_mapping = {
+ RayJobDeploymentStatus.COMPLETE: (CodeflareRayJobStatus.COMPLETE, True),
+ RayJobDeploymentStatus.RUNNING: (CodeflareRayJobStatus.RUNNING, False),
+ RayJobDeploymentStatus.FAILED: (CodeflareRayJobStatus.FAILED, False),
+ RayJobDeploymentStatus.SUSPENDED: (CodeflareRayJobStatus.SUSPENDED, False),
+ }
+
+ return status_mapping.get(
+ deployment_status, (CodeflareRayJobStatus.UNKNOWN, False)
+ )
+
+ def _extract_script_files_from_entrypoint(self) -> Optional[Dict[str, str]]:
+ """
+ Extract local Python script files from entrypoint command, plus their dependencies.
+
+ Returns:
+ Dict of {script_name: script_content} if local scripts found, None otherwise
+ """
+ if not self.entrypoint:
+ return None
+
+ scripts = {}
+ processed_files = set() # Avoid infinite loops
+
+ # Look for Python file patterns in entrypoint (e.g., "python script.py", "python /path/to/script.py")
+ python_file_pattern = r"(?:python\s+)?([./\w/]+\.py)"
+ matches = re.findall(python_file_pattern, self.entrypoint)
+
+ # Process main scripts from entrypoint files
+ for script_path in matches:
+ self._process_script_and_imports(
+ script_path, scripts, MOUNT_PATH, processed_files
+ )
+
+ # Update entrypoint paths to use mounted locations
+ for script_path in matches:
+ if script_path in [os.path.basename(s) for s in processed_files]:
+ old_path = script_path
+ new_path = f"{MOUNT_PATH}/{os.path.basename(script_path)}"
+ self.entrypoint = self.entrypoint.replace(old_path, new_path)
+
+ return scripts if scripts else None
+
+ def _process_script_and_imports(
+ self,
+ script_path: str,
+ scripts: Dict[str, str],
+ mount_path: str,
+ processed_files: set,
+ ):
+ """Recursively process a script and its local imports"""
+ if script_path in processed_files:
+ return
+
+ # Check if it's a local file (not already a container path)
+ if script_path.startswith("/home/ray/") or not os.path.isfile(script_path):
+ return
+
+ processed_files.add(script_path)
+
+ try:
+ with open(script_path, "r") as f:
+ script_content = f.read()
+
+ script_name = os.path.basename(script_path)
+ scripts[script_name] = script_content
+
+ logger.info(
+ f"Found local script: {script_path} -> will mount at {mount_path}/{script_name}"
+ )
+
+ # Parse imports in this script to find dependencies
+ self._find_local_imports(
+ script_content,
+ script_path,
+ lambda path: self._process_script_and_imports(
+ path, scripts, mount_path, processed_files
+ ),
+ )
+
+ except (IOError, OSError) as e:
+ logger.warning(f"Could not read script file {script_path}: {e}")
+
+ def _find_local_imports(
+ self, script_content: str, script_path: str, process_callback
+ ):
+ """
+ Find local Python imports in script content and process them.
+
+ Args:
+ script_content: The content of the Python script
+ script_path: Path to the current script (for relative imports)
+ process_callback: Function to call for each found local import
+ """
+
+ try:
+ # Parse the Python AST to find imports
+ tree = ast.parse(script_content)
+ script_dir = os.path.dirname(os.path.abspath(script_path))
+
+ for node in ast.walk(tree):
+ if isinstance(node, ast.Import):
+ # Handle: import module_name
+ for alias in node.names:
+ potential_file = os.path.join(script_dir, f"{alias.name}.py")
+ if os.path.isfile(potential_file):
+ process_callback(potential_file)
+
+ elif isinstance(node, ast.ImportFrom):
+ # Handle: from module_name import something
+ if node.module:
+ potential_file = os.path.join(script_dir, f"{node.module}.py")
+ if os.path.isfile(potential_file):
+ process_callback(potential_file)
+
+ except (SyntaxError, ValueError) as e:
+ logger.debug(f"Could not parse imports from {script_path}: {e}")
+
+ def _handle_script_volumes_for_new_cluster(
+ self, scripts: Dict[str, str], rayjob_result: Dict[str, Any] = None
+ ):
+ """Handle script volumes for new clusters (uses ManagedClusterConfig)."""
+ # Validate ConfigMap size before creation
+ self._cluster_config.validate_configmap_size(scripts)
+
+ # Build ConfigMap spec using config.py
+ configmap_spec = self._cluster_config.build_script_configmap_spec(
+ job_name=self.name, namespace=self.namespace, scripts=scripts
+ )
+
+ # Create ConfigMap via Kubernetes API with owner reference
+ configmap_name = self._create_configmap_from_spec(configmap_spec, rayjob_result)
+
+ # Add volumes to cluster config (config.py handles spec building)
+ self._cluster_config.add_script_volumes(
+ configmap_name=configmap_name, mount_path=MOUNT_PATH
+ )
+
+ def _handle_script_volumes_for_existing_cluster(
+ self, scripts: Dict[str, str], rayjob_result: Dict[str, Any] = None
+ ):
+ """Handle script volumes for existing clusters (updates RayCluster CR)."""
+ # Create config builder for utility methods
+ config_builder = ManagedClusterConfig()
+
+ # Validate ConfigMap size before creation
+ config_builder.validate_configmap_size(scripts)
+
+ # Build ConfigMap spec using config.py
+ configmap_spec = config_builder.build_script_configmap_spec(
+ job_name=self.name, namespace=self.namespace, scripts=scripts
+ )
+
+ # Create ConfigMap via Kubernetes API with owner reference
+ configmap_name = self._create_configmap_from_spec(configmap_spec, rayjob_result)
+
+ # Update existing RayCluster
+ self._update_existing_cluster_for_scripts(configmap_name, config_builder)
+
+ def _create_configmap_from_spec(
+ self, configmap_spec: Dict[str, Any], rayjob_result: Dict[str, Any] = None
+ ) -> str:
+ """
+ Create ConfigMap from specification via Kubernetes API.
+
+ Args:
+ configmap_spec: ConfigMap specification dictionary
+ rayjob_result: The result from RayJob creation containing UID
+
+ Returns:
+ str: Name of the created ConfigMap
+ """
+
+ configmap_name = configmap_spec["metadata"]["name"]
+
+ metadata = client.V1ObjectMeta(**configmap_spec["metadata"])
+
+ # Add owner reference if we have the RayJob result
+ if (
+ rayjob_result
+ and isinstance(rayjob_result, dict)
+ and rayjob_result.get("metadata", {}).get("uid")
+ ):
+ logger.info(
+ f"Adding owner reference to ConfigMap '{configmap_name}' with RayJob UID: {rayjob_result['metadata']['uid']}"
+ )
+ metadata.owner_references = [
+ client.V1OwnerReference(
+ api_version="ray.io/v1",
+ kind="RayJob",
+ name=self.name,
+ uid=rayjob_result["metadata"]["uid"],
+ controller=True,
+ block_owner_deletion=True,
+ )
+ ]
+ else:
+ logger.warning(
+ f"No valid RayJob result with UID found, ConfigMap '{configmap_name}' will not have owner reference. Result: {rayjob_result}"
+ )
+
+ # Convert dict spec to V1ConfigMap
+ configmap = client.V1ConfigMap(
+ metadata=metadata,
+ data=configmap_spec["data"],
+ )
+
+ # Create ConfigMap via Kubernetes API
+ k8s_api = client.CoreV1Api(get_api_client())
+ try:
+ k8s_api.create_namespaced_config_map(
+ namespace=self.namespace, body=configmap
+ )
+ logger.info(
+ f"Created ConfigMap '{configmap_name}' with {len(configmap_spec['data'])} scripts"
+ )
+ except client.ApiException as e:
+ if e.status == 409: # Already exists
+ logger.info(f"ConfigMap '{configmap_name}' already exists, updating...")
+ k8s_api.replace_namespaced_config_map(
+ name=configmap_name, namespace=self.namespace, body=configmap
+ )
+ else:
+ raise RuntimeError(
+ f"Failed to create ConfigMap '{configmap_name}': {e}"
+ )
+
+ return configmap_name
+
+ # Note: This only works once the pods have been restarted as the configmaps won't be picked up until then :/
+ def _update_existing_cluster_for_scripts(
+ self, configmap_name: str, config_builder: ManagedClusterConfig
+ ):
+ """
+ Update existing RayCluster to add script volumes and mounts.
+
+ Args:
+ configmap_name: Name of the ConfigMap containing scripts
+ config_builder: ManagedClusterConfig instance for building specs
+ """
+
+ try:
+ ray_cluster = self._cluster_api.get_ray_cluster(
+ name=self.cluster_name,
+ k8s_namespace=self.namespace,
+ )
+ except client.ApiException as e:
+ raise RuntimeError(f"Failed to get RayCluster '{self.cluster_name}': {e}")
+
+ # Build script volume and mount specifications using config.py
+ script_volume, script_mount = config_builder.build_script_volume_specs(
+ configmap_name=configmap_name, mount_path=MOUNT_PATH
+ )
+
+ # Helper function to check for duplicate volumes/mounts
+ def volume_exists(volumes_list, volume_name):
+ return any(v.get("name") == volume_name for v in volumes_list)
+
+ def mount_exists(mounts_list, mount_name):
+ return any(m.get("name") == mount_name for m in mounts_list)
+
+ # Add volumes and mounts to head group
+ head_spec = ray_cluster["spec"]["headGroupSpec"]["template"]["spec"]
+ if "volumes" not in head_spec:
+ head_spec["volumes"] = []
+ if not volume_exists(head_spec["volumes"], script_volume["name"]):
+ head_spec["volumes"].append(script_volume)
+
+ head_container = head_spec["containers"][0] # Ray head container
+ if "volumeMounts" not in head_container:
+ head_container["volumeMounts"] = []
+ if not mount_exists(head_container["volumeMounts"], script_mount["name"]):
+ head_container["volumeMounts"].append(script_mount)
+
+ # Add volumes and mounts to worker groups
+ for worker_group in ray_cluster["spec"]["workerGroupSpecs"]:
+ worker_spec = worker_group["template"]["spec"]
+ if "volumes" not in worker_spec:
+ worker_spec["volumes"] = []
+ if not volume_exists(worker_spec["volumes"], script_volume["name"]):
+ worker_spec["volumes"].append(script_volume)
+
+ worker_container = worker_spec["containers"][0] # Ray worker container
+ if "volumeMounts" not in worker_container:
+ worker_container["volumeMounts"] = []
+ if not mount_exists(worker_container["volumeMounts"], script_mount["name"]):
+ worker_container["volumeMounts"].append(script_mount)
+
+ # Update the RayCluster
+ try:
+ self._cluster_api.patch_ray_cluster(
+ name=self.cluster_name,
+ ray_patch=ray_cluster,
+ k8s_namespace=self.namespace,
+ )
+ logger.info(
+ f"Updated RayCluster '{self.cluster_name}' with script volumes from ConfigMap '{configmap_name}'"
+ )
+ except client.ApiException as e:
+ raise RuntimeError(
+ f"Failed to update RayCluster '{self.cluster_name}': {e}"
+ )
diff --git a/src/codeflare_sdk/ray/rayjobs/status.py b/src/codeflare_sdk/ray/rayjobs/status.py
new file mode 100644
index 00000000..027ed09c
--- /dev/null
+++ b/src/codeflare_sdk/ray/rayjobs/status.py
@@ -0,0 +1,64 @@
+# Copyright 2025 IBM, Red Hat
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+The status sub-module defines Enums containing information for Ray job
+deployment states and CodeFlare job states, as well as
+dataclasses to store information for Ray jobs.
+"""
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import Optional
+
+
+class RayJobDeploymentStatus(Enum):
+ """
+ Defines the possible deployment states of a Ray job (from the KubeRay RayJob API).
+ """
+
+ COMPLETE = "Complete"
+ RUNNING = "Running"
+ FAILED = "Failed"
+ SUSPENDED = "Suspended"
+ UNKNOWN = "Unknown"
+
+
+class CodeflareRayJobStatus(Enum):
+ """
+ Defines the possible reportable states of a CodeFlare Ray job.
+ """
+
+ COMPLETE = 1
+ RUNNING = 2
+ FAILED = 3
+ SUSPENDED = 4
+ UNKNOWN = 5
+
+
+@dataclass
+class RayJobInfo:
+ """
+ For storing information about a Ray job.
+ """
+
+ name: str
+ job_id: str
+ status: RayJobDeploymentStatus
+ namespace: str
+ cluster_name: str
+ start_time: Optional[str] = None
+ end_time: Optional[str] = None
+ failed_attempts: int = 0
+ succeeded_attempts: int = 0
diff --git a/src/codeflare_sdk/ray/rayjobs/test_config.py b/src/codeflare_sdk/ray/rayjobs/test_config.py
new file mode 100644
index 00000000..d19864ba
--- /dev/null
+++ b/src/codeflare_sdk/ray/rayjobs/test_config.py
@@ -0,0 +1,229 @@
+"""
+Tests for the simplified ManagedClusterConfig accelerator_configs behavior.
+"""
+
+import pytest
+from codeflare_sdk.ray.rayjobs.config import ManagedClusterConfig, DEFAULT_ACCELERATORS
+
+
+def test_accelerator_configs_defaults_to_default_accelerators():
+ """Test that accelerator_configs defaults to DEFAULT_ACCELERATORS.copy()"""
+ config = ManagedClusterConfig()
+
+ # Should have all the default accelerators
+ assert "nvidia.com/gpu" in config.accelerator_configs
+ assert "intel.com/gpu" in config.accelerator_configs
+ assert "google.com/tpu" in config.accelerator_configs
+
+ # Should be a copy, not the same object
+ assert config.accelerator_configs is not DEFAULT_ACCELERATORS
+ assert config.accelerator_configs == DEFAULT_ACCELERATORS
+
+
+def test_accelerator_configs_can_be_overridden():
+ """Test that users can override accelerator_configs with custom mappings"""
+ custom_configs = {
+ "nvidia.com/gpu": "GPU",
+ "custom.com/accelerator": "CUSTOM_ACCELERATOR",
+ }
+
+ config = ManagedClusterConfig(accelerator_configs=custom_configs)
+
+ # Should have custom configs
+ assert config.accelerator_configs == custom_configs
+ assert "custom.com/accelerator" in config.accelerator_configs
+ assert "nvidia.com/gpu" in config.accelerator_configs
+
+ # Should NOT have other defaults
+ assert "intel.com/gpu" not in config.accelerator_configs
+ assert "google.com/tpu" not in config.accelerator_configs
+
+
+def test_accelerator_configs_can_extend_defaults():
+ """Test that users can extend defaults by providing additional configs"""
+ extended_configs = {
+ **DEFAULT_ACCELERATORS,
+ "custom.com/accelerator": "CUSTOM_ACCEL",
+ }
+
+ config = ManagedClusterConfig(accelerator_configs=extended_configs)
+
+ # Should have all defaults plus custom
+ assert "nvidia.com/gpu" in config.accelerator_configs
+ assert "intel.com/gpu" in config.accelerator_configs
+ assert "custom.com/accelerator" in config.accelerator_configs
+ assert config.accelerator_configs["custom.com/accelerator"] == "CUSTOM_ACCEL"
+
+
+def test_gpu_validation_works_with_defaults():
+ """Test that GPU validation works with default accelerator configs"""
+ config = ManagedClusterConfig(head_accelerators={"nvidia.com/gpu": 1})
+
+ # Should not raise any errors
+ assert config.head_accelerators == {"nvidia.com/gpu": 1}
+
+
+def test_gpu_validation_works_with_custom_configs():
+ """Test that GPU validation works with custom accelerator configs"""
+ config = ManagedClusterConfig(
+ accelerator_configs={"custom.com/accelerator": "CUSTOM_ACCEL"},
+ head_accelerators={"custom.com/accelerator": 1},
+ )
+
+ # Should not raise any errors
+ assert config.head_accelerators == {"custom.com/accelerator": 1}
+
+
+def test_gpu_validation_fails_with_unsupported_accelerator():
+ """Test that GPU validation fails with unsupported accelerators"""
+ with pytest.raises(
+ ValueError, match="GPU configuration 'unsupported.com/accelerator' not found"
+ ):
+ ManagedClusterConfig(head_accelerators={"unsupported.com/accelerator": 1})
+
+
+def test_config_type_validation_errors(mocker):
+ """Test that type validation properly raises errors with incorrect types."""
+ # Mock the _is_type method to return False for type checking
+ mocker.patch.object(
+ ManagedClusterConfig,
+ "_is_type",
+ side_effect=lambda value, expected_type: False, # Always fail type check
+ )
+
+ # This should raise TypeError during initialization
+ with pytest.raises(TypeError, match="Type validation failed"):
+ ManagedClusterConfig()
+
+
+def test_config_is_type_method():
+ """Test the _is_type static method for type checking."""
+ # Test basic types
+ assert ManagedClusterConfig._is_type("test", str) is True
+ assert ManagedClusterConfig._is_type(123, int) is True
+ assert ManagedClusterConfig._is_type(123, str) is False
+
+ # Test optional types (Union with None)
+ from typing import Optional
+
+ assert ManagedClusterConfig._is_type(None, Optional[str]) is True
+ assert ManagedClusterConfig._is_type("test", Optional[str]) is True
+ assert ManagedClusterConfig._is_type(123, Optional[str]) is False
+
+ # Test dict types
+ assert ManagedClusterConfig._is_type({}, dict) is True
+ assert ManagedClusterConfig._is_type({"key": "value"}, dict) is True
+ assert ManagedClusterConfig._is_type([], dict) is False
+
+
+def test_ray_usage_stats_always_disabled_by_default():
+ """Test that RAY_USAGE_STATS_ENABLED is always set to '0' by default"""
+ config = ManagedClusterConfig()
+
+ # Should always have the environment variable set to "0"
+ assert "RAY_USAGE_STATS_ENABLED" in config.envs
+ assert config.envs["RAY_USAGE_STATS_ENABLED"] == "0"
+
+
+def test_ray_usage_stats_overwrites_user_env():
+ """Test that RAY_USAGE_STATS_ENABLED is always set to '0' even if user specifies it"""
+ # User tries to enable usage stats
+ config = ManagedClusterConfig(envs={"RAY_USAGE_STATS_ENABLED": "1"})
+
+ # Should still be disabled (our setting takes precedence)
+ assert "RAY_USAGE_STATS_ENABLED" in config.envs
+ assert config.envs["RAY_USAGE_STATS_ENABLED"] == "0"
+
+
+def test_ray_usage_stats_overwrites_user_env_string():
+ """Test that RAY_USAGE_STATS_ENABLED is always set to '0' even if user specifies it as string"""
+ # User tries to enable usage stats with string
+ config = ManagedClusterConfig(envs={"RAY_USAGE_STATS_ENABLED": "true"})
+
+ # Should still be disabled (our setting takes precedence)
+ assert "RAY_USAGE_STATS_ENABLED" in config.envs
+ assert config.envs["RAY_USAGE_STATS_ENABLED"] == "0"
+
+
+def test_ray_usage_stats_with_other_user_envs():
+ """Test that RAY_USAGE_STATS_ENABLED is set correctly while preserving other user envs"""
+ # User sets other environment variables
+ user_envs = {
+ "CUSTOM_VAR": "custom_value",
+ "ANOTHER_VAR": "another_value",
+ "RAY_USAGE_STATS_ENABLED": "1", # This should be overwritten
+ }
+
+ config = ManagedClusterConfig(envs=user_envs)
+
+ # Our setting should take precedence
+ assert config.envs["RAY_USAGE_STATS_ENABLED"] == "0"
+
+ # Other user envs should be preserved
+ assert config.envs["CUSTOM_VAR"] == "custom_value"
+ assert config.envs["ANOTHER_VAR"] == "another_value"
+
+ # Total count should be correct (3 user envs)
+ assert len(config.envs) == 3
+
+
+def test_add_script_volumes_existing_volume_early_return():
+ """Test add_script_volumes early return when volume already exists."""
+ from kubernetes.client import V1Volume, V1ConfigMapVolumeSource
+
+ config = ManagedClusterConfig()
+
+ # Pre-add a volume with same name
+ existing_volume = V1Volume(
+ name="ray-job-scripts",
+ config_map=V1ConfigMapVolumeSource(name="existing-scripts"),
+ )
+ config.volumes.append(existing_volume)
+
+ # Should return early and not add duplicate
+ config.add_script_volumes(configmap_name="new-scripts")
+
+ # Should still have only one volume, no mount added
+ assert len(config.volumes) == 1
+ assert len(config.volume_mounts) == 0
+
+
+def test_add_script_volumes_existing_mount_early_return():
+ """Test add_script_volumes early return when mount already exists."""
+ from kubernetes.client import V1VolumeMount
+
+ config = ManagedClusterConfig()
+
+ # Pre-add a mount with same name
+ existing_mount = V1VolumeMount(name="ray-job-scripts", mount_path="/existing/path")
+ config.volume_mounts.append(existing_mount)
+
+ # Should return early and not add duplicate
+ config.add_script_volumes(configmap_name="new-scripts")
+
+ # Should still have only one mount, no volume added
+ assert len(config.volumes) == 0
+ assert len(config.volume_mounts) == 1
+
+
+def test_build_script_configmap_spec_labels():
+ """Test that build_script_configmap_spec creates ConfigMap with correct labels."""
+ config = ManagedClusterConfig()
+
+ job_name = "test-job"
+ namespace = "test-namespace"
+ scripts = {"script.py": "print('hello')", "helper.py": "# helper code"}
+
+ configmap_spec = config.build_script_configmap_spec(job_name, namespace, scripts)
+
+ assert configmap_spec["apiVersion"] == "v1"
+ assert configmap_spec["kind"] == "ConfigMap"
+ assert configmap_spec["metadata"]["name"] == f"{job_name}-scripts"
+ assert configmap_spec["metadata"]["namespace"] == namespace
+
+ labels = configmap_spec["metadata"]["labels"]
+ assert labels["ray.io/job-name"] == job_name
+ assert labels["app.kubernetes.io/managed-by"] == "codeflare-sdk"
+ assert labels["app.kubernetes.io/component"] == "rayjob-scripts"
+
+ assert configmap_spec["data"] == scripts
diff --git a/src/codeflare_sdk/ray/rayjobs/test_pretty_print.py b/src/codeflare_sdk/ray/rayjobs/test_pretty_print.py
new file mode 100644
index 00000000..3bbe8bee
--- /dev/null
+++ b/src/codeflare_sdk/ray/rayjobs/test_pretty_print.py
@@ -0,0 +1,265 @@
+# Copyright 2025 IBM, Red Hat
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from codeflare_sdk.ray.rayjobs.pretty_print import (
+ _get_status_display,
+ print_job_status,
+ print_no_job_found,
+)
+from codeflare_sdk.ray.rayjobs.status import RayJobDeploymentStatus, RayJobInfo
+from unittest.mock import MagicMock, call
+
+
+def test_get_status_display():
+ """
+ Test the _get_status_display function.
+ """
+ # Test Complete status
+ display, color = _get_status_display(RayJobDeploymentStatus.COMPLETE)
+ assert display == "Complete :white_heavy_check_mark:"
+ assert color == "[white on green][bold]Name"
+
+ # Test Running status
+ display, color = _get_status_display(RayJobDeploymentStatus.RUNNING)
+ assert display == "Running :gear:"
+ assert color == "[white on blue][bold]Name"
+
+ # Test Failed status
+ display, color = _get_status_display(RayJobDeploymentStatus.FAILED)
+ assert display == "Failed :x:"
+ assert color == "[white on red][bold]Name"
+
+ # Test Suspended status
+ display, color = _get_status_display(RayJobDeploymentStatus.SUSPENDED)
+ assert display == "Suspended :pause_button:"
+ assert color == "[white on yellow][bold]Name"
+
+ # Test Unknown status
+ display, color = _get_status_display(RayJobDeploymentStatus.UNKNOWN)
+ assert display == "Unknown :question:"
+ assert color == "[white on red][bold]Name"
+
+
+def test_print_job_status_running_format(mocker):
+ """
+ Test the print_job_status function format for a running job.
+ """
+ # Mock Rich components to verify format
+ mock_console = MagicMock()
+ mock_inner_table = MagicMock()
+ mock_main_table = MagicMock()
+ mock_panel = MagicMock()
+
+ # Mock Table to return different instances for inner and main tables
+ table_instances = [mock_inner_table, mock_main_table]
+ mock_table_class = MagicMock(side_effect=table_instances)
+
+ mocker.patch(
+ "codeflare_sdk.ray.rayjobs.pretty_print.Console", return_value=mock_console
+ )
+ mocker.patch("codeflare_sdk.ray.rayjobs.pretty_print.Table", mock_table_class)
+ mocker.patch("codeflare_sdk.ray.rayjobs.pretty_print.Panel", mock_panel)
+
+ # Create test job info for running job
+ job_info = RayJobInfo(
+ name="test-job",
+ job_id="test-job-abc123",
+ status=RayJobDeploymentStatus.RUNNING,
+ namespace="test-ns",
+ cluster_name="test-cluster",
+ start_time="2025-07-28T11:37:07Z",
+ failed_attempts=0,
+ succeeded_attempts=0,
+ )
+
+ # Call the function
+ print_job_status(job_info)
+
+ # Verify both Table calls
+ expected_table_calls = [
+ call(box=None, show_header=False), # Inner content table
+ call(
+ box=None, title="[bold] :package: CodeFlare RayJob Status :package:"
+ ), # Main wrapper table
+ ]
+ mock_table_class.assert_has_calls(expected_table_calls)
+
+ # Verify inner table rows are added in correct order and format (versus our hard-coded version of this for cluster)
+ expected_calls = [
+ call("[white on blue][bold]Name"), # Header with blue color for running
+ call(
+ "[bold underline]test-job", "Running :gear:"
+ ), # Name and status with gear emoji
+ call(), # Empty separator row
+ call("[bold]Job ID:[/bold] test-job-abc123"),
+ call("[bold]Status:[/bold] Running"),
+ call("[bold]RayCluster:[/bold] test-cluster"),
+ call("[bold]Namespace:[/bold] test-ns"),
+ call(), # Empty row before timing info
+ call("[bold]Started:[/bold] 2025-07-28T11:37:07Z"),
+ ]
+ mock_inner_table.add_row.assert_has_calls(expected_calls)
+
+ # Verify Panel is created with inner table
+ mock_panel.fit.assert_called_once_with(mock_inner_table)
+
+ # Verify main table gets the panel
+ mock_main_table.add_row.assert_called_once_with(mock_panel.fit.return_value)
+
+ # Verify console prints the main table
+ mock_console.print.assert_called_once_with(mock_main_table)
+
+
+def test_print_job_status_complete_format(mocker):
+ """
+ Test the print_job_status function format for a completed job.
+ """
+ # Mock Rich components
+ mock_console = MagicMock()
+ mock_inner_table = MagicMock()
+ mock_main_table = MagicMock()
+ mock_panel = MagicMock()
+
+ # Mock Table to return different instances
+ table_instances = [mock_inner_table, mock_main_table]
+ mock_table_class = MagicMock(side_effect=table_instances)
+
+ mocker.patch(
+ "codeflare_sdk.ray.rayjobs.pretty_print.Console", return_value=mock_console
+ )
+ mocker.patch("codeflare_sdk.ray.rayjobs.pretty_print.Table", mock_table_class)
+ mocker.patch("codeflare_sdk.ray.rayjobs.pretty_print.Panel", mock_panel)
+
+ # Create test job info for completed job
+ job_info = RayJobInfo(
+ name="completed-job",
+ job_id="completed-job-xyz789",
+ status=RayJobDeploymentStatus.COMPLETE,
+ namespace="prod-ns",
+ cluster_name="prod-cluster",
+ start_time="2025-07-28T11:37:07Z",
+ failed_attempts=0,
+ succeeded_attempts=1,
+ )
+
+ # Call the function
+ print_job_status(job_info)
+
+ # Verify correct header color for completed job (green) (versus our hard-coded version of this for cluster)
+ expected_calls = [
+ call("[white on green][bold]Name"), # Green header for complete
+ call(
+ "[bold underline]completed-job", "Complete :white_heavy_check_mark:"
+ ), # Checkmark emoji
+ call(), # Empty separator
+ call("[bold]Job ID:[/bold] completed-job-xyz789"),
+ call("[bold]Status:[/bold] Complete"),
+ call("[bold]RayCluster:[/bold] prod-cluster"),
+ call("[bold]Namespace:[/bold] prod-ns"),
+ call(), # Empty row before timing info
+ call("[bold]Started:[/bold] 2025-07-28T11:37:07Z"),
+ ]
+ mock_inner_table.add_row.assert_has_calls(expected_calls)
+
+
+def test_print_job_status_failed_with_attempts_format(mocker):
+ """
+ Test the print_job_status function format for a failed job with attempts.
+ """
+ # Mock Rich components
+ mock_console = MagicMock()
+ mock_inner_table = MagicMock()
+ mock_main_table = MagicMock()
+ mock_panel = MagicMock()
+
+ # Mock Table to return different instances
+ table_instances = [mock_inner_table, mock_main_table]
+ mock_table_class = MagicMock(side_effect=table_instances)
+
+ mocker.patch(
+ "codeflare_sdk.ray.rayjobs.pretty_print.Console", return_value=mock_console
+ )
+ mocker.patch("codeflare_sdk.ray.rayjobs.pretty_print.Table", mock_table_class)
+ mocker.patch("codeflare_sdk.ray.rayjobs.pretty_print.Panel", mock_panel)
+
+ # Create test job info with failures
+ job_info = RayJobInfo(
+ name="failing-job",
+ job_id="failing-job-fail123",
+ status=RayJobDeploymentStatus.FAILED,
+ namespace="test-ns",
+ cluster_name="test-cluster",
+ start_time="2025-07-28T11:37:07Z",
+ failed_attempts=3, # Has failures
+ succeeded_attempts=0,
+ )
+
+ # Call the function
+ print_job_status(job_info)
+
+ # Verify correct formatting including failure attempts (versus our hard-coded version of this for cluster)
+ expected_calls = [
+ call("[white on red][bold]Name"), # Red header for failed
+ call("[bold underline]failing-job", "Failed :x:"), # X emoji for failed
+ call(), # Empty separator
+ call("[bold]Job ID:[/bold] failing-job-fail123"),
+ call("[bold]Status:[/bold] Failed"),
+ call("[bold]RayCluster:[/bold] test-cluster"),
+ call("[bold]Namespace:[/bold] test-ns"),
+ call(), # Empty row before timing info
+ call("[bold]Started:[/bold] 2025-07-28T11:37:07Z"),
+ call("[bold]Failed Attempts:[/bold] 3"), # Failed attempts should be shown
+ ]
+ mock_inner_table.add_row.assert_has_calls(expected_calls)
+
+
+def test_print_no_job_found_format(mocker):
+ """
+ Test the print_no_job_found function format.
+ """
+ # Mock Rich components
+ mock_console = MagicMock()
+ mock_inner_table = MagicMock()
+ mock_main_table = MagicMock()
+ mock_panel = MagicMock()
+
+ # Mock Table to return different instances
+ table_instances = [mock_inner_table, mock_main_table]
+ mock_table_class = MagicMock(side_effect=table_instances)
+
+ mocker.patch(
+ "codeflare_sdk.ray.rayjobs.pretty_print.Console", return_value=mock_console
+ )
+ mocker.patch("codeflare_sdk.ray.rayjobs.pretty_print.Table", mock_table_class)
+ mocker.patch("codeflare_sdk.ray.rayjobs.pretty_print.Panel", mock_panel)
+
+ # Call the function
+ print_no_job_found("missing-job", "test-namespace")
+
+ # Verify error message format (versus our hard-coded version of this for cluster)
+ expected_calls = [
+ call("[white on red][bold]Name"), # Red header for error
+ call(
+ "[bold underline]missing-job", "[bold red]No RayJob found"
+ ), # Error message in red
+ call(), # Empty separator
+ call(), # Another empty row
+ call("Please run rayjob.submit() to submit a job."), # Helpful hint
+ call(), # Empty separator
+ call("[bold]Namespace:[/bold] test-namespace"),
+ ]
+ mock_inner_table.add_row.assert_has_calls(expected_calls)
+
+ # Verify Panel is used
+ mock_panel.fit.assert_called_once_with(mock_inner_table)
diff --git a/src/codeflare_sdk/ray/rayjobs/test_rayjob.py b/src/codeflare_sdk/ray/rayjobs/test_rayjob.py
new file mode 100644
index 00000000..829265d6
--- /dev/null
+++ b/src/codeflare_sdk/ray/rayjobs/test_rayjob.py
@@ -0,0 +1,2196 @@
+# Copyright 2022-2025 IBM, Red Hat
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+import os
+from unittest.mock import MagicMock, patch
+from codeflare_sdk.common.utils.constants import MOUNT_PATH, RAY_VERSION
+
+from codeflare_sdk.ray.rayjobs.rayjob import RayJob
+from codeflare_sdk.ray.cluster.config import ClusterConfiguration
+from codeflare_sdk.ray.rayjobs.config import ManagedClusterConfig
+from kubernetes.client import (
+ V1Volume,
+ V1VolumeMount,
+ V1Toleration,
+ V1ConfigMapVolumeSource,
+ ApiException,
+)
+
+
+# Global test setup that runs automatically for ALL tests
+@pytest.fixture(autouse=True)
+def auto_mock_setup(mocker):
+ """Automatically mock common dependencies for all tests."""
+ mocker.patch("kubernetes.config.load_kube_config")
+
+ # Always mock get_default_kueue_name to prevent K8s API calls
+ mocker.patch(
+ "codeflare_sdk.ray.rayjobs.rayjob.get_default_kueue_name",
+ return_value="default-queue",
+ )
+
+ mock_get_ns = mocker.patch(
+ "codeflare_sdk.ray.rayjobs.rayjob.get_current_namespace",
+ return_value="test-namespace",
+ )
+
+ mock_rayjob_api = mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayjobApi")
+ mock_rayjob_instance = MagicMock()
+ mock_rayjob_api.return_value = mock_rayjob_instance
+
+ mock_cluster_api = mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayClusterApi")
+ mock_cluster_instance = MagicMock()
+ mock_cluster_api.return_value = mock_cluster_instance
+
+ mock_k8s_api = mocker.patch("kubernetes.client.CoreV1Api")
+ mock_k8s_instance = MagicMock()
+ mock_k8s_api.return_value = mock_k8s_instance
+
+ mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.get_api_client")
+
+ # Return the mocked instances so tests can configure them as needed
+ return {
+ "rayjob_api": mock_rayjob_instance,
+ "cluster_api": mock_cluster_instance,
+ "k8s_api": mock_k8s_instance,
+ "get_current_namespace": mock_get_ns,
+ }
+
+
+def test_rayjob_submit_success(auto_mock_setup):
+ """Test successful RayJob submission."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+
+ mock_api_instance.submit.return_value = {"metadata": {"name": "test-rayjob"}}
+
+ rayjob = RayJob(
+ job_name="test-rayjob",
+ cluster_name="test-ray-cluster",
+ namespace="test-namespace",
+ entrypoint="python -c 'print(\"hello world\")'",
+ runtime_env={"pip": ["requests"]},
+ )
+
+ job_id = rayjob.submit()
+
+ assert job_id == "test-rayjob"
+
+ mock_api_instance.submit_job.assert_called_once()
+ call_args = mock_api_instance.submit_job.call_args
+
+ assert call_args.kwargs["k8s_namespace"] == "test-namespace"
+
+ job_cr = call_args.kwargs["job"]
+ assert job_cr["metadata"]["name"] == "test-rayjob"
+ assert job_cr["metadata"]["namespace"] == "test-namespace"
+ assert job_cr["spec"]["entrypoint"] == "python -c 'print(\"hello world\")'"
+ assert job_cr["spec"]["clusterSelector"]["ray.io/cluster"] == "test-ray-cluster"
+ assert job_cr["spec"]["runtimeEnvYAML"] == "{'pip': ['requests']}"
+
+
+def test_rayjob_submit_failure(auto_mock_setup):
+ """Test RayJob submission failure."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+
+ mock_api_instance.submit_job.return_value = None
+
+ rayjob = RayJob(
+ job_name="test-rayjob",
+ cluster_name="test-ray-cluster",
+ namespace="default",
+ entrypoint="python script.py",
+ runtime_env={"pip": ["numpy"]},
+ )
+
+ with pytest.raises(RuntimeError, match="Failed to submit RayJob test-rayjob"):
+ rayjob.submit()
+
+
+def test_rayjob_init_validation_both_provided(auto_mock_setup):
+ """Test that providing both cluster_name and cluster_config raises error."""
+ cluster_config = ClusterConfiguration(name="test-cluster", namespace="test")
+
+ with pytest.raises(
+ ValueError,
+ match="❌ Configuration Error: You cannot specify both 'cluster_name' and 'cluster_config'",
+ ):
+ RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ cluster_config=cluster_config,
+ entrypoint="python script.py",
+ )
+
+
+def test_rayjob_init_validation_neither_provided(auto_mock_setup):
+ """Test that providing neither cluster_name nor cluster_config raises error."""
+ with pytest.raises(
+ ValueError,
+ match="❌ Configuration Error: You must provide either 'cluster_name'",
+ ):
+ RayJob(job_name="test-job", entrypoint="python script.py")
+
+
+def test_rayjob_init_with_cluster_config(auto_mock_setup):
+ """Test RayJob initialization with cluster configuration for auto-creation."""
+ cluster_config = ClusterConfiguration(
+ name="auto-cluster", namespace="test-namespace", num_workers=2
+ )
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python script.py",
+ namespace="test-namespace",
+ )
+
+ assert rayjob.name == "test-job"
+ assert rayjob.cluster_name == "test-job-cluster" # Generated from job name
+ assert rayjob._cluster_config == cluster_config
+ assert rayjob._cluster_name is None
+
+
+def test_rayjob_cluster_name_generation(auto_mock_setup):
+ """Test that cluster names are generated when config has empty name."""
+ cluster_config = ClusterConfiguration(
+ name="", # Empty name should trigger generation
+ namespace="test-namespace",
+ num_workers=1,
+ )
+
+ rayjob = RayJob(
+ job_name="my-job",
+ cluster_config=cluster_config,
+ entrypoint="python script.py",
+ namespace="test-namespace",
+ )
+
+ assert rayjob.cluster_name == "my-job-cluster"
+
+
+def test_rayjob_cluster_config_namespace_none(auto_mock_setup):
+ """Test that cluster config namespace is set when None."""
+ cluster_config = ClusterConfiguration(
+ name="test-cluster",
+ namespace=None, # This should be set to job namespace
+ num_workers=1,
+ )
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ namespace="job-namespace",
+ entrypoint="python script.py",
+ )
+
+ assert rayjob.namespace == "job-namespace"
+
+
+def test_rayjob_with_active_deadline_seconds(auto_mock_setup):
+ """Test RayJob CR generation with active deadline seconds."""
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="test-cluster",
+ namespace="test-namespace",
+ entrypoint="python main.py",
+ active_deadline_seconds=30,
+ )
+
+ rayjob_cr = rayjob._build_rayjob_cr()
+
+ assert rayjob_cr["spec"]["activeDeadlineSeconds"] == 30
+
+
+def test_build_ray_cluster_spec_no_config_error(auto_mock_setup):
+ """Test _build_ray_cluster_spec raises error when no cluster config."""
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python script.py",
+ namespace="test-namespace",
+ )
+
+ rayjob_cr = rayjob._build_rayjob_cr()
+
+ assert rayjob_cr["spec"]["clusterSelector"]["ray.io/cluster"] == "existing-cluster"
+ assert "rayClusterSpec" not in rayjob_cr["spec"]
+
+
+def test_build_ray_cluster_spec(mocker, auto_mock_setup):
+ """Test _build_ray_cluster_spec method."""
+
+ mock_ray_cluster = {
+ "apiVersion": "ray.io/v1",
+ "kind": "RayCluster",
+ "metadata": {"name": "test-cluster", "namespace": "test"},
+ "spec": {
+ "rayVersion": RAY_VERSION,
+ "headGroupSpec": {"replicas": 1},
+ "workerGroupSpecs": [{"replicas": 2}],
+ },
+ }
+ cluster_config = ManagedClusterConfig(num_workers=2)
+ mocker.patch.object(
+ cluster_config, "build_ray_cluster_spec", return_value=mock_ray_cluster["spec"]
+ )
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python script.py",
+ namespace="test-namespace",
+ )
+
+ rayjob_cr = rayjob._build_rayjob_cr()
+
+ assert "rayClusterSpec" in rayjob_cr["spec"]
+ cluster_config.build_ray_cluster_spec.assert_called_once_with(
+ cluster_name="test-job-cluster"
+ )
+
+
+def test_build_rayjob_cr_with_existing_cluster(auto_mock_setup):
+ """Test _build_rayjob_cr method with existing cluster."""
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ namespace="test-namespace",
+ entrypoint="python main.py",
+ ttl_seconds_after_finished=300,
+ )
+
+ rayjob_cr = rayjob._build_rayjob_cr()
+
+ assert rayjob_cr["apiVersion"] == "ray.io/v1"
+ assert rayjob_cr["kind"] == "RayJob"
+ assert rayjob_cr["metadata"]["name"] == "test-job"
+ spec = rayjob_cr["spec"]
+ assert spec["entrypoint"] == "python main.py"
+ assert spec["shutdownAfterJobFinishes"] is False
+ assert spec["ttlSecondsAfterFinished"] == 300
+
+ assert spec["clusterSelector"]["ray.io/cluster"] == "existing-cluster"
+ assert "rayClusterSpec" not in spec
+
+
+def test_build_rayjob_cr_with_auto_cluster(mocker, auto_mock_setup):
+ """Test _build_rayjob_cr method with auto-created cluster."""
+ mock_ray_cluster = {
+ "apiVersion": "ray.io/v1",
+ "kind": "RayCluster",
+ "metadata": {"name": "auto-cluster", "namespace": "test"},
+ "spec": {
+ "rayVersion": RAY_VERSION,
+ "headGroupSpec": {"replicas": 1},
+ "workerGroupSpecs": [{"replicas": 2}],
+ },
+ }
+ cluster_config = ManagedClusterConfig(num_workers=2)
+
+ mocker.patch.object(
+ cluster_config, "build_ray_cluster_spec", return_value=mock_ray_cluster["spec"]
+ )
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python main.py",
+ namespace="test-namespace",
+ )
+
+ rayjob_cr = rayjob._build_rayjob_cr()
+ assert rayjob_cr["spec"]["rayClusterSpec"] == mock_ray_cluster["spec"]
+ assert "clusterSelector" not in rayjob_cr["spec"]
+
+
+def test_submit_validation_no_entrypoint(auto_mock_setup):
+ """Test that submit() raises error when entrypoint is None."""
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="test-cluster",
+ entrypoint=None, # No entrypoint provided
+ namespace="test-namespace",
+ )
+
+ with pytest.raises(
+ ValueError, match="Entrypoint must be provided to submit a RayJob"
+ ):
+ rayjob.submit()
+
+
+def test_submit_with_auto_cluster(mocker, auto_mock_setup):
+ """Test successful submission with auto-created cluster."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+
+ mock_ray_cluster = {
+ "apiVersion": "ray.io/v1",
+ "kind": "RayCluster",
+ "spec": {
+ "rayVersion": RAY_VERSION,
+ "headGroupSpec": {"replicas": 1},
+ "workerGroupSpecs": [{"replicas": 1}],
+ },
+ }
+ mock_api_instance.submit_job.return_value = True
+
+ cluster_config = ManagedClusterConfig(num_workers=1)
+ mocker.patch.object(
+ cluster_config, "build_ray_cluster_spec", return_value=mock_ray_cluster["spec"]
+ )
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python script.py",
+ namespace="test-namespace",
+ )
+
+ result = rayjob.submit()
+
+ assert result == "test-job"
+
+ mock_api_instance.submit_job.assert_called_once()
+ call_args = mock_api_instance.submit_job.call_args
+
+ job_cr = call_args.kwargs["job"]
+ assert "rayClusterSpec" in job_cr["spec"]
+ assert job_cr["spec"]["rayClusterSpec"] == mock_ray_cluster["spec"]
+
+
+def test_namespace_auto_detection_success(auto_mock_setup):
+ """Test successful namespace auto-detection."""
+ auto_mock_setup["get_current_namespace"].return_value = "detected-ns"
+
+ rayjob = RayJob(
+ job_name="test-job", entrypoint="python script.py", cluster_name="test-cluster"
+ )
+
+ assert rayjob.namespace == "detected-ns"
+
+
+def test_namespace_auto_detection_fallback(auto_mock_setup):
+ """Test that namespace auto-detection failure raises an error."""
+ auto_mock_setup["get_current_namespace"].return_value = None
+
+ with pytest.raises(ValueError, match="Could not auto-detect Kubernetes namespace"):
+ RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_name="test-cluster",
+ )
+
+
+def test_namespace_explicit_override(auto_mock_setup):
+ """Test that explicit namespace overrides auto-detection."""
+ auto_mock_setup["get_current_namespace"].return_value = "detected-ns"
+
+ rayjob = RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_name="test-cluster",
+ namespace="explicit-ns",
+ )
+
+ assert rayjob.namespace == "explicit-ns"
+
+
+def test_rayjob_with_rayjob_cluster_config(auto_mock_setup):
+ """Test RayJob with the new ManagedClusterConfig."""
+ cluster_config = ManagedClusterConfig(
+ num_workers=2,
+ head_cpu_requests="500m",
+ head_memory_requests="512Mi",
+ )
+
+ rayjob = RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_config=cluster_config,
+ namespace="test-namespace",
+ )
+
+ assert rayjob._cluster_config == cluster_config
+ assert rayjob.cluster_name == "test-job-cluster" # Generated from job name
+
+
+def test_rayjob_cluster_config_validation(auto_mock_setup):
+ """Test validation of ManagedClusterConfig parameters."""
+ cluster_config = ManagedClusterConfig()
+
+ rayjob = RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_config=cluster_config,
+ namespace="test-namespace",
+ )
+
+ assert rayjob._cluster_config is not None
+
+
+def test_rayjob_missing_entrypoint_validation(auto_mock_setup):
+ """Test that RayJob requires entrypoint for submission."""
+ with pytest.raises(
+ TypeError, match="missing 1 required positional argument: 'entrypoint'"
+ ):
+ RayJob(
+ job_name="test-job",
+ cluster_name="test-cluster",
+ )
+
+
+def test_build_ray_cluster_spec_integration(mocker, auto_mock_setup):
+ """Test integration with the new build_ray_cluster_spec method."""
+ cluster_config = ManagedClusterConfig()
+ mock_spec = {"spec": "test-spec"}
+ mocker.patch.object(
+ cluster_config, "build_ray_cluster_spec", return_value=mock_spec
+ )
+
+ rayjob = RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_config=cluster_config,
+ namespace="test-namespace",
+ )
+
+ rayjob_cr = rayjob._build_rayjob_cr()
+
+ cluster_config.build_ray_cluster_spec.assert_called_once_with(
+ cluster_name="test-job-cluster"
+ )
+ assert "rayClusterSpec" in rayjob_cr["spec"]
+ assert rayjob_cr["spec"]["rayClusterSpec"] == mock_spec
+
+
+def test_rayjob_with_runtime_env(auto_mock_setup):
+ """Test RayJob with runtime environment configuration."""
+ runtime_env = {"pip": ["numpy", "pandas"]}
+
+ rayjob = RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_name="test-cluster",
+ runtime_env=runtime_env,
+ namespace="test-namespace",
+ )
+
+ assert rayjob.runtime_env == runtime_env
+
+ rayjob_cr = rayjob._build_rayjob_cr()
+ assert rayjob_cr["spec"]["runtimeEnvYAML"] == str(runtime_env)
+
+
+def test_rayjob_with_active_deadline_and_ttl(auto_mock_setup):
+ """Test RayJob with both active deadline and TTL settings."""
+
+ rayjob = RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_name="test-cluster",
+ active_deadline_seconds=300,
+ ttl_seconds_after_finished=600,
+ namespace="test-namespace",
+ )
+
+ assert rayjob.active_deadline_seconds == 300
+ assert rayjob.ttl_seconds_after_finished == 600
+
+ rayjob_cr = rayjob._build_rayjob_cr()
+ assert rayjob_cr["spec"]["activeDeadlineSeconds"] == 300
+ assert rayjob_cr["spec"]["ttlSecondsAfterFinished"] == 600
+
+
+def test_rayjob_cluster_name_generation_with_config(auto_mock_setup):
+ """Test cluster name generation when using cluster_config."""
+
+ cluster_config = ManagedClusterConfig()
+
+ rayjob = RayJob(
+ job_name="my-job",
+ entrypoint="python script.py",
+ cluster_config=cluster_config,
+ namespace="test-namespace", # Explicitly specify namespace
+ )
+
+ assert rayjob.cluster_name == "my-job-cluster"
+
+
+def test_rayjob_namespace_propagation_to_cluster_config(auto_mock_setup):
+ """Test that job namespace is propagated to cluster config when None."""
+ auto_mock_setup["get_current_namespace"].return_value = "detected-ns"
+
+ cluster_config = ManagedClusterConfig()
+
+ rayjob = RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_config=cluster_config,
+ )
+
+ assert rayjob.namespace == "detected-ns"
+
+
+def test_rayjob_error_handling_invalid_cluster_config(auto_mock_setup):
+ """Test error handling with invalid cluster configuration."""
+
+ with pytest.raises(ValueError):
+ RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ )
+
+
+def test_rayjob_constructor_parameter_validation(auto_mock_setup):
+ """Test constructor parameter validation."""
+ rayjob = RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_name="test-cluster",
+ namespace="test-ns",
+ runtime_env={"pip": ["numpy"]},
+ ttl_seconds_after_finished=300,
+ active_deadline_seconds=600,
+ )
+
+ assert rayjob.name == "test-job"
+ assert rayjob.entrypoint == "python script.py"
+ assert rayjob.cluster_name == "test-cluster"
+ assert rayjob.namespace == "test-ns"
+ assert rayjob.runtime_env == {"pip": ["numpy"]}
+ assert rayjob.ttl_seconds_after_finished == 300
+ assert rayjob.active_deadline_seconds == 600
+
+
+def test_build_ray_cluster_spec_function():
+ """Test the build_ray_cluster_spec method directly."""
+ cluster_config = ManagedClusterConfig(
+ num_workers=2,
+ head_cpu_requests="500m",
+ head_memory_requests="512Mi",
+ worker_cpu_requests="250m",
+ worker_memory_requests="256Mi",
+ )
+
+ spec = cluster_config.build_ray_cluster_spec("test-cluster")
+ assert "rayVersion" in spec
+ assert "headGroupSpec" in spec
+ assert "workerGroupSpecs" in spec
+
+ head_spec = spec["headGroupSpec"]
+ assert head_spec["serviceType"] == "ClusterIP"
+ assert head_spec["enableIngress"] is False
+ assert "rayStartParams" in head_spec
+ assert "template" in head_spec
+ worker_specs = spec["workerGroupSpecs"]
+ assert len(worker_specs) == 1
+ worker_spec = worker_specs[0]
+ assert worker_spec["replicas"] == 2
+ assert worker_spec["minReplicas"] == 2
+ assert worker_spec["maxReplicas"] == 2
+ assert worker_spec["groupName"] == "worker-group-test-cluster"
+
+
+def test_build_ray_cluster_spec_with_accelerators():
+ """Test build_ray_cluster_spec with GPU accelerators."""
+ cluster_config = ManagedClusterConfig(
+ head_accelerators={"nvidia.com/gpu": 1},
+ worker_accelerators={"nvidia.com/gpu": 2},
+ )
+
+ spec = cluster_config.build_ray_cluster_spec("test-cluster")
+ head_spec = spec["headGroupSpec"]
+ head_params = head_spec["rayStartParams"]
+ assert "num-gpus" in head_params
+ assert head_params["num-gpus"] == "1"
+
+ worker_specs = spec["workerGroupSpecs"]
+ worker_spec = worker_specs[0]
+ worker_params = worker_spec["rayStartParams"]
+ assert "num-gpus" in worker_params
+ assert worker_params["num-gpus"] == "2"
+
+
+def test_build_ray_cluster_spec_with_custom_volumes():
+ """Test build_ray_cluster_spec with custom volumes and volume mounts."""
+ custom_volume = V1Volume(name="custom-data", empty_dir={})
+ custom_volume_mount = V1VolumeMount(name="custom-data", mount_path="/data")
+ cluster_config = ManagedClusterConfig(
+ volumes=[custom_volume],
+ volume_mounts=[custom_volume_mount],
+ )
+
+ spec = cluster_config.build_ray_cluster_spec("test-cluster")
+ head_spec = spec["headGroupSpec"]
+ head_pod_spec = head_spec["template"].spec
+ assert len(head_pod_spec.volumes) > 0
+
+ head_container = head_pod_spec.containers[0]
+ assert len(head_container.volume_mounts) > 0
+
+
+def test_build_ray_cluster_spec_with_environment_variables():
+ """Test build_ray_cluster_spec with environment variables."""
+ cluster_config = ManagedClusterConfig(
+ envs={"CUDA_VISIBLE_DEVICES": "0", "RAY_DISABLE_IMPORT_WARNING": "1"},
+ )
+
+ spec = cluster_config.build_ray_cluster_spec("test-cluster")
+
+ head_spec = spec["headGroupSpec"]
+ head_pod_spec = head_spec["template"].spec
+ head_container = head_pod_spec.containers[0]
+ assert hasattr(head_container, "env")
+ env_vars = {env.name: env.value for env in head_container.env}
+ assert env_vars["CUDA_VISIBLE_DEVICES"] == "0"
+ assert env_vars["RAY_DISABLE_IMPORT_WARNING"] == "1"
+ worker_specs = spec["workerGroupSpecs"]
+ worker_spec = worker_specs[0]
+ worker_pod_spec = worker_spec["template"].spec
+ worker_container = worker_pod_spec.containers[0]
+
+ assert hasattr(worker_container, "env")
+ worker_env_vars = {env.name: env.value for env in worker_container.env}
+ assert worker_env_vars["CUDA_VISIBLE_DEVICES"] == "0"
+ assert worker_env_vars["RAY_DISABLE_IMPORT_WARNING"] == "1"
+
+
+def test_build_ray_cluster_spec_with_tolerations():
+ """Test build_ray_cluster_spec with tolerations."""
+ head_toleration = V1Toleration(
+ key="node-role.kubernetes.io/master", operator="Exists", effect="NoSchedule"
+ )
+ worker_toleration = V1Toleration(
+ key="nvidia.com/gpu", operator="Exists", effect="NoSchedule"
+ )
+
+ cluster_config = ManagedClusterConfig(
+ head_tolerations=[head_toleration],
+ worker_tolerations=[worker_toleration],
+ )
+
+ spec = cluster_config.build_ray_cluster_spec("test-cluster")
+ head_spec = spec["headGroupSpec"]
+ head_pod_spec = head_spec["template"].spec
+ assert hasattr(head_pod_spec, "tolerations")
+ assert len(head_pod_spec.tolerations) == 1
+ assert head_pod_spec.tolerations[0].key == "node-role.kubernetes.io/master"
+
+ worker_specs = spec["workerGroupSpecs"]
+ worker_spec = worker_specs[0]
+ worker_pod_spec = worker_spec["template"].spec
+ assert hasattr(worker_pod_spec, "tolerations")
+ assert len(worker_pod_spec.tolerations) == 1
+ assert worker_pod_spec.tolerations[0].key == "nvidia.com/gpu"
+
+
+def test_build_ray_cluster_spec_with_image_pull_secrets():
+ """Test build_ray_cluster_spec with image pull secrets."""
+ cluster_config = ManagedClusterConfig(
+ image_pull_secrets=["my-registry-secret", "another-secret"]
+ )
+
+ spec = cluster_config.build_ray_cluster_spec("test-cluster")
+
+ head_spec = spec["headGroupSpec"]
+ head_pod_spec = head_spec["template"].spec
+ assert hasattr(head_pod_spec, "image_pull_secrets")
+
+ head_secrets = head_pod_spec.image_pull_secrets
+ assert len(head_secrets) == 2
+ assert head_secrets[0].name == "my-registry-secret"
+ assert head_secrets[1].name == "another-secret"
+
+ worker_specs = spec["workerGroupSpecs"]
+ worker_spec = worker_specs[0]
+ worker_pod_spec = worker_spec["template"].spec
+ assert hasattr(worker_pod_spec, "image_pull_secrets")
+
+ worker_secrets = worker_pod_spec.image_pull_secrets
+ assert len(worker_secrets) == 2
+ assert worker_secrets[0].name == "my-registry-secret"
+ assert worker_secrets[1].name == "another-secret"
+
+
+class TestRayVersionValidation:
+ """Test Ray version validation in RayJob."""
+
+ def test_submit_with_cluster_config_compatible_image_passes(self, auto_mock_setup):
+ """Test that submission passes with compatible cluster_config image."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+ mock_api_instance.submit_job.return_value = True
+
+ cluster_config = ManagedClusterConfig(image=f"ray:{RAY_VERSION}")
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ namespace="test-namespace",
+ entrypoint="python script.py",
+ )
+
+ result = rayjob.submit()
+ assert result == "test-job"
+
+ def test_submit_with_cluster_config_incompatible_image_fails(self, auto_mock_setup):
+ """Test that submission fails with incompatible cluster_config image."""
+ #
+
+ cluster_config = ManagedClusterConfig(image="ray:2.8.0") # Different version
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ namespace="test-namespace",
+ entrypoint="python script.py",
+ )
+
+ with pytest.raises(
+ ValueError, match="Cluster config image: Ray version mismatch detected"
+ ):
+ rayjob.submit()
+
+ def test_validate_ray_version_compatibility_method(self, auto_mock_setup):
+ """Test the _validate_ray_version_compatibility method directly."""
+ #
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="test-cluster",
+ namespace="test-namespace",
+ entrypoint="python script.py",
+ )
+
+ rayjob._validate_ray_version_compatibility()
+ rayjob._cluster_config = ManagedClusterConfig(image=f"ray:{RAY_VERSION}")
+ rayjob._validate_ray_version_compatibility()
+ rayjob._cluster_config = ManagedClusterConfig(image="ray:2.8.0")
+ with pytest.raises(
+ ValueError, match="Cluster config image: Ray version mismatch detected"
+ ):
+ rayjob._validate_ray_version_compatibility()
+
+ rayjob._cluster_config = ManagedClusterConfig(image="custom-image:latest")
+ with pytest.warns(
+ UserWarning, match="Cluster config image: Cannot determine Ray version"
+ ):
+ rayjob._validate_ray_version_compatibility()
+
+ def test_validate_cluster_config_image_method(self, auto_mock_setup):
+ """Test the _validate_cluster_config_image method directly."""
+ #
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=ManagedClusterConfig(),
+ namespace="test-namespace",
+ entrypoint="python script.py",
+ )
+
+ rayjob._validate_cluster_config_image()
+ rayjob._cluster_config.image = f"ray:{RAY_VERSION}"
+ rayjob._validate_cluster_config_image()
+ rayjob._cluster_config.image = "ray:2.8.0"
+ with pytest.raises(
+ ValueError, match="Cluster config image: Ray version mismatch detected"
+ ):
+ rayjob._validate_cluster_config_image()
+
+ rayjob._cluster_config.image = "custom-image:latest"
+ with pytest.warns(
+ UserWarning, match="Cluster config image: Cannot determine Ray version"
+ ):
+ rayjob._validate_cluster_config_image()
+
+ def test_validate_cluster_config_image_edge_cases(self, auto_mock_setup):
+ """Test edge cases in _validate_cluster_config_image method."""
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=ManagedClusterConfig(),
+ namespace="test-namespace",
+ entrypoint="python script.py",
+ )
+
+ rayjob._cluster_config.image = None
+ rayjob._validate_cluster_config_image()
+ rayjob._cluster_config.image = ""
+ rayjob._validate_cluster_config_image()
+ rayjob._cluster_config.image = 123
+ rayjob._validate_cluster_config_image()
+
+ class MockClusterConfig:
+ pass
+
+ rayjob._cluster_config = MockClusterConfig()
+ rayjob._validate_cluster_config_image()
+
+
+def test_extract_script_files_from_entrypoint_single_script(auto_mock_setup, tmp_path):
+ """Test extracting a single script file from entrypoint."""
+
+ # Create a test script
+ test_script = tmp_path / "test_script.py"
+ test_script.write_text("print('Hello World!')")
+
+ # Change to temp directory for test
+ original_cwd = os.getcwd()
+ os.chdir(tmp_path)
+
+ try:
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint=f"python {test_script.name}",
+ namespace="test-namespace",
+ )
+
+ scripts = rayjob._extract_script_files_from_entrypoint()
+
+ assert scripts is not None
+ assert test_script.name in scripts
+ assert scripts[test_script.name] == "print('Hello World!')"
+ assert f"{MOUNT_PATH}/{test_script.name}" in rayjob.entrypoint
+ finally:
+ os.chdir(original_cwd)
+
+
+def test_extract_script_files_with_dependencies(auto_mock_setup, tmp_path):
+ """Test extracting script files with local dependencies."""
+
+ # Create main script and dependency
+ main_script = tmp_path / "main.py"
+ main_script.write_text(
+ """
+import helper
+from utils import calculate
+
+def main():
+ helper.do_something()
+ result = calculate(42)
+ print(f"Result: {result}")
+
+if __name__ == "__main__":
+ main()
+"""
+ )
+
+ helper_script = tmp_path / "helper.py"
+ helper_script.write_text(
+ """
+def do_something():
+ print("Doing something...")
+"""
+ )
+
+ utils_script = tmp_path / "utils.py"
+ utils_script.write_text(
+ """
+def calculate(x):
+ return x * 2
+"""
+ )
+
+ # Change to temp directory for test
+ original_cwd = os.getcwd()
+ os.chdir(tmp_path)
+
+ try:
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python main.py",
+ namespace="test-namespace",
+ )
+
+ scripts = rayjob._extract_script_files_from_entrypoint()
+
+ assert scripts is not None
+ assert len(scripts) == 3
+ assert "main.py" in scripts
+ assert "helper.py" in scripts
+ assert "utils.py" in scripts
+
+ assert "import helper" in scripts["main.py"]
+ assert "def do_something" in scripts["helper.py"]
+ assert "def calculate" in scripts["utils.py"]
+
+ finally:
+ os.chdir(original_cwd)
+
+
+def test_extract_script_files_no_local_scripts(auto_mock_setup):
+ """Test entrypoint with no local script files."""
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python -c 'print(\"hello world\")'",
+ namespace="test-namespace",
+ )
+
+ scripts = rayjob._extract_script_files_from_entrypoint()
+
+ assert scripts is None
+
+
+def test_extract_script_files_nonexistent_script(auto_mock_setup):
+ """Test entrypoint referencing non-existent script."""
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python nonexistent.py",
+ namespace="test-namespace",
+ )
+
+ scripts = rayjob._extract_script_files_from_entrypoint()
+
+ assert scripts is None
+
+
+def test_build_script_configmap_spec():
+ """Test building ConfigMap specification for scripts."""
+ config = ManagedClusterConfig()
+ scripts = {"main.py": "print('main')", "helper.py": "print('helper')"}
+
+ spec = config.build_script_configmap_spec(
+ job_name="test-job", namespace="test-namespace", scripts=scripts
+ )
+
+ assert spec["apiVersion"] == "v1"
+ assert spec["kind"] == "ConfigMap"
+ assert spec["metadata"]["name"] == "test-job-scripts"
+ assert spec["metadata"]["namespace"] == "test-namespace"
+ assert spec["data"] == scripts
+
+
+def test_build_script_volume_specs():
+ """Test building volume and mount specifications for scripts."""
+ config = ManagedClusterConfig()
+
+ volume_spec, mount_spec = config.build_script_volume_specs(
+ configmap_name="test-scripts", mount_path="/custom/path"
+ )
+
+ assert volume_spec["name"] == "ray-job-scripts"
+ assert volume_spec["configMap"]["name"] == "test-scripts"
+
+ assert mount_spec["name"] == "ray-job-scripts"
+ assert mount_spec["mountPath"] == "/custom/path"
+
+
+def test_add_script_volumes():
+ """Test adding script volumes to cluster configuration."""
+ config = ManagedClusterConfig()
+
+ # Initially no volumes
+ assert len(config.volumes) == 0
+ assert len(config.volume_mounts) == 0
+
+ config.add_script_volumes(configmap_name="test-scripts")
+
+ assert len(config.volumes) == 1
+ assert len(config.volume_mounts) == 1
+
+ volume = config.volumes[0]
+ mount = config.volume_mounts[0]
+
+ assert volume.name == "ray-job-scripts"
+ assert volume.config_map.name == "test-scripts"
+
+ assert mount.name == "ray-job-scripts"
+ assert mount.mount_path == MOUNT_PATH
+
+
+def test_add_script_volumes_duplicate_prevention():
+ """Test that adding script volumes twice doesn't create duplicates."""
+ config = ManagedClusterConfig()
+
+ # Add volumes twice
+ config.add_script_volumes(configmap_name="test-scripts")
+ config.add_script_volumes(configmap_name="test-scripts")
+
+ assert len(config.volumes) == 1
+ assert len(config.volume_mounts) == 1
+
+
+def test_create_configmap_from_spec(auto_mock_setup):
+ """Test creating ConfigMap via Kubernetes API."""
+ mock_api_instance = auto_mock_setup["k8s_api"]
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ configmap_spec = {
+ "apiVersion": "v1",
+ "kind": "ConfigMap",
+ "metadata": {"name": "test-scripts", "namespace": "test-namespace"},
+ "data": {"test.py": "print('test')"},
+ }
+
+ result = rayjob._create_configmap_from_spec(configmap_spec)
+
+ assert result == "test-scripts"
+ mock_api_instance.create_namespaced_config_map.assert_called_once()
+
+
+def test_create_configmap_already_exists(auto_mock_setup):
+ """Test creating ConfigMap when it already exists (409 conflict)."""
+ mock_api_instance = auto_mock_setup["k8s_api"]
+
+ mock_api_instance.create_namespaced_config_map.side_effect = ApiException(
+ status=409
+ )
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ configmap_spec = {
+ "apiVersion": "v1",
+ "kind": "ConfigMap",
+ "metadata": {"name": "test-scripts", "namespace": "test-namespace"},
+ "data": {"test.py": "print('test')"},
+ }
+
+ result = rayjob._create_configmap_from_spec(configmap_spec)
+
+ assert result == "test-scripts"
+ mock_api_instance.create_namespaced_config_map.assert_called_once()
+ mock_api_instance.replace_namespaced_config_map.assert_called_once()
+
+
+def test_create_configmap_with_owner_reference_basic(mocker, auto_mock_setup, caplog):
+ """Test creating ConfigMap with owner reference from valid RayJob result."""
+ mock_api_instance = auto_mock_setup["k8s_api"]
+
+ # Mock client.V1ObjectMeta and V1ConfigMap
+ mock_v1_metadata = mocker.patch("kubernetes.client.V1ObjectMeta")
+ mock_metadata_instance = MagicMock()
+ mock_v1_metadata.return_value = mock_metadata_instance
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ configmap_spec = {
+ "apiVersion": "v1",
+ "kind": "ConfigMap",
+ "metadata": {
+ "name": "test-scripts",
+ "namespace": "test-namespace",
+ "labels": {
+ "ray.io/job-name": "test-job",
+ "app.kubernetes.io/managed-by": "codeflare-sdk",
+ "app.kubernetes.io/component": "rayjob-scripts",
+ },
+ },
+ "data": {"test.py": "print('test')"},
+ }
+
+ # Valid RayJob result with UID
+ rayjob_result = {
+ "metadata": {
+ "name": "test-job",
+ "namespace": "test-namespace",
+ "uid": "a4dd4c5a-ab61-411d-b4d1-4abb5177422a",
+ }
+ }
+
+ with caplog.at_level("INFO"):
+ result = rayjob._create_configmap_from_spec(configmap_spec, rayjob_result)
+
+ assert result == "test-scripts"
+
+ # Verify owner reference was set
+ expected_owner_ref = mocker.ANY # We'll check via the logs
+ assert (
+ "Adding owner reference to ConfigMap 'test-scripts' with RayJob UID: a4dd4c5a-ab61-411d-b4d1-4abb5177422a"
+ in caplog.text
+ )
+
+ assert mock_metadata_instance.owner_references is not None
+ mock_api_instance.create_namespaced_config_map.assert_called_once()
+
+
+def test_create_configmap_without_owner_reference_no_uid(
+ mocker, auto_mock_setup, caplog
+):
+ """Test creating ConfigMap without owner reference when RayJob has no UID."""
+ mock_api_instance = auto_mock_setup["k8s_api"]
+
+ mock_v1_metadata = mocker.patch("kubernetes.client.V1ObjectMeta")
+ mock_metadata_instance = MagicMock()
+ mock_v1_metadata.return_value = mock_metadata_instance
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ configmap_spec = {
+ "apiVersion": "v1",
+ "kind": "ConfigMap",
+ "metadata": {"name": "test-scripts", "namespace": "test-namespace"},
+ "data": {"test.py": "print('test')"},
+ }
+
+ # RayJob result without UID
+ rayjob_result = {
+ "metadata": {
+ "name": "test-job",
+ "namespace": "test-namespace",
+ # No UID field
+ }
+ }
+
+ with caplog.at_level("WARNING"):
+ result = rayjob._create_configmap_from_spec(configmap_spec, rayjob_result)
+
+ assert result == "test-scripts"
+
+ # Verify warning was logged and no owner reference was set
+ assert (
+ "No valid RayJob result with UID found, ConfigMap 'test-scripts' will not have owner reference"
+ in caplog.text
+ )
+
+ # The important part is that the warning was logged, indicating no owner reference was set
+ mock_api_instance.create_namespaced_config_map.assert_called_once()
+
+
+def test_create_configmap_with_invalid_rayjob_result(auto_mock_setup, caplog):
+ """Test creating ConfigMap with None or invalid rayjob_result."""
+ mock_api_instance = auto_mock_setup["k8s_api"]
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ configmap_spec = {
+ "apiVersion": "v1",
+ "kind": "ConfigMap",
+ "metadata": {"name": "test-scripts", "namespace": "test-namespace"},
+ "data": {"test.py": "print('test')"},
+ }
+
+ # Test with None
+ with caplog.at_level("WARNING"):
+ result = rayjob._create_configmap_from_spec(configmap_spec, None)
+
+ assert result == "test-scripts"
+ assert "No valid RayJob result with UID found" in caplog.text
+
+ # Test with string instead of dict
+ caplog.clear()
+ with caplog.at_level("WARNING"):
+ result = rayjob._create_configmap_from_spec(configmap_spec, "not-a-dict")
+
+ assert result == "test-scripts"
+ assert "No valid RayJob result with UID found" in caplog.text
+
+
+def test_handle_script_volumes_for_new_cluster(mocker, auto_mock_setup, tmp_path):
+ """Test handling script volumes for new cluster creation."""
+ # auto_mock_setup handles kubernetes and API mocking
+
+ mock_create = mocker.patch.object(RayJob, "_create_configmap_from_spec")
+ mock_create.return_value = "test-job-scripts"
+
+ test_script = tmp_path / "test.py"
+ test_script.write_text("print('test')")
+
+ cluster_config = ManagedClusterConfig()
+
+ original_cwd = os.getcwd()
+ os.chdir(tmp_path)
+
+ try:
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ scripts = {"test.py": "print('test')"}
+ rayjob._handle_script_volumes_for_new_cluster(scripts)
+
+ mock_create.assert_called_once()
+
+ assert len(cluster_config.volumes) == 1
+ assert len(cluster_config.volume_mounts) == 1
+
+ finally:
+ os.chdir(original_cwd)
+
+
+def test_ast_parsing_import_detection(auto_mock_setup, tmp_path):
+ """Test AST parsing correctly detects import statements."""
+
+ main_script = tmp_path / "main.py"
+ main_script.write_text(
+ """# Different import patterns
+import helper
+from utils import func1, func2
+from local_module import MyClass
+import os # Standard library - should be ignored
+import non_existent # Non-local - should be ignored
+"""
+ )
+
+ helper_script = tmp_path / "helper.py"
+ helper_script.write_text("def helper_func(): pass")
+
+ utils_script = tmp_path / "utils.py"
+ utils_script.write_text(
+ """def func1(): pass
+def func2(): pass
+"""
+ )
+
+ local_module_script = tmp_path / "local_module.py"
+ local_module_script.write_text("class MyClass: pass")
+
+ original_cwd = os.getcwd()
+ os.chdir(tmp_path)
+
+ try:
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python main.py",
+ namespace="test-namespace",
+ )
+
+ scripts = rayjob._extract_script_files_from_entrypoint()
+
+ assert scripts is not None
+ assert len(scripts) == 4 # main + 3 dependencies
+ assert "main.py" in scripts
+ assert "helper.py" in scripts
+ assert "utils.py" in scripts
+ assert "local_module.py" in scripts
+
+ finally:
+ os.chdir(original_cwd)
+
+
+def test_script_handling_timing_after_rayjob_submission(
+ mocker, auto_mock_setup, tmp_path
+):
+ """Test that script handling happens after RayJob is submitted (not before)."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+
+ submit_result = {
+ "metadata": {
+ "name": "test-job",
+ "namespace": "test-namespace",
+ "uid": "test-uid-12345",
+ }
+ }
+ mock_api_instance.submit_job.return_value = submit_result
+
+ mock_handle_new = mocker.patch.object(
+ RayJob, "_handle_script_volumes_for_new_cluster"
+ )
+
+ # RayClusterApi is already mocked by auto_mock_setup
+
+ test_script = tmp_path / "test.py"
+ test_script.write_text("print('test')")
+
+ call_order = []
+
+ def track_submit(*args, **kwargs):
+ call_order.append("submit_job")
+ return submit_result
+
+ def track_handle_scripts(*args, **kwargs):
+ call_order.append("handle_scripts")
+ assert len(args) >= 2
+ assert args[1] == submit_result # rayjob_result should be second arg
+
+ mock_api_instance.submit_job.side_effect = track_submit
+ mock_handle_new.side_effect = track_handle_scripts
+
+ original_cwd = os.getcwd()
+ try:
+ os.chdir(tmp_path)
+
+ cluster_config = ManagedClusterConfig()
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ rayjob.submit()
+ finally:
+ os.chdir(original_cwd)
+
+ assert call_order == ["submit_job", "handle_scripts"]
+
+ mock_api_instance.submit_job.assert_called_once()
+ mock_handle_new.assert_called_once()
+
+ mock_handle_new.assert_called_with({"test.py": "print('test')"}, submit_result)
+
+
+def test_rayjob_submit_with_scripts_new_cluster(auto_mock_setup, tmp_path):
+ """Test RayJob submission with script detection for new cluster."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+ mock_api_instance.submit_job.return_value = True
+
+ mock_k8s_instance = auto_mock_setup["k8s_api"]
+
+ # Create test script
+ test_script = tmp_path / "test.py"
+ test_script.write_text("print('Hello from script!')")
+
+ cluster_config = ManagedClusterConfig()
+
+ original_cwd = os.getcwd()
+ os.chdir(tmp_path)
+
+ try:
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ # Submit should detect scripts and handle them
+ result = rayjob.submit()
+
+ assert result == "test-job"
+
+ mock_k8s_instance.create_namespaced_config_map.assert_called_once()
+
+ assert len(cluster_config.volumes) == 1
+ assert len(cluster_config.volume_mounts) == 1
+ assert f"{MOUNT_PATH}/test.py" in rayjob.entrypoint
+
+ finally:
+ os.chdir(original_cwd)
+
+
+def test_process_script_and_imports_io_error(mocker, auto_mock_setup, tmp_path):
+ """Test _process_script_and_imports handles IO errors gracefully."""
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ scripts = {}
+ processed_files = set()
+
+ # Mock os.path.isfile to return True but open() to raise IOError
+ mocker.patch("os.path.isfile", return_value=True)
+ mocker.patch("builtins.open", side_effect=IOError("Permission denied"))
+
+ rayjob._process_script_and_imports("test.py", scripts, MOUNT_PATH, processed_files)
+ assert "test.py" in processed_files
+ assert len(scripts) == 0
+
+
+def test_process_script_and_imports_container_path_skip(auto_mock_setup):
+ """Test that scripts already in container paths are skipped."""
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ scripts = {}
+ processed_files = set()
+
+ # Test script path already in container
+ rayjob._process_script_and_imports(
+ f"{MOUNT_PATH}/test.py", scripts, MOUNT_PATH, processed_files
+ )
+
+ assert len(scripts) == 0
+ assert len(processed_files) == 0
+
+
+def test_process_script_and_imports_already_processed(auto_mock_setup, tmp_path):
+ """Test that already processed scripts are skipped (infinite loop prevention)."""
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ scripts = {}
+ processed_files = {"test.py"} # Already processed
+
+ rayjob._process_script_and_imports("test.py", scripts, MOUNT_PATH, processed_files)
+
+ assert len(scripts) == 0
+ assert processed_files == {"test.py"}
+
+
+def test_submit_with_scripts_owner_reference_integration(
+ mocker, auto_mock_setup, tmp_path, caplog
+):
+ """Integration test for submit() with local scripts to verify end-to-end owner reference flow."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+ mock_k8s_instance = auto_mock_setup["k8s_api"]
+
+ # RayJob submission returns result with UID
+ submit_result = {
+ "metadata": {
+ "name": "test-job",
+ "namespace": "test-namespace",
+ "uid": "unique-rayjob-uid-12345",
+ }
+ }
+ mock_api_instance.submit_job.return_value = submit_result
+
+ # Capture the ConfigMap that gets created
+ created_configmap = None
+
+ def capture_configmap(namespace, body):
+ nonlocal created_configmap
+ created_configmap = body
+ return body
+
+ mock_k8s_instance.create_namespaced_config_map.side_effect = capture_configmap
+
+ # Create test scripts
+ test_script = tmp_path / "main.py"
+ test_script.write_text("import helper\nprint('main')")
+
+ helper_script = tmp_path / "helper.py"
+ helper_script.write_text("def help(): print('helper')")
+
+ # Change to temp directory for script detection
+ original_cwd = os.getcwd()
+ try:
+ os.chdir(tmp_path)
+
+ cluster_config = ManagedClusterConfig()
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python main.py",
+ namespace="test-namespace",
+ )
+
+ with caplog.at_level("INFO"):
+ result = rayjob.submit()
+
+ assert result == "test-job"
+
+ mock_api_instance.submit_job.assert_called_once()
+ mock_k8s_instance.create_namespaced_config_map.assert_called_once()
+ assert created_configmap is not None
+
+ # Verify owner reference was set correctly
+ assert hasattr(created_configmap.metadata, "owner_references")
+ assert created_configmap.metadata.owner_references is not None
+ assert len(created_configmap.metadata.owner_references) == 1
+
+ owner_ref = created_configmap.metadata.owner_references[0]
+ assert owner_ref.api_version == "ray.io/v1"
+ assert owner_ref.kind == "RayJob"
+ assert owner_ref.name == "test-job"
+ assert owner_ref.uid == "unique-rayjob-uid-12345"
+ assert owner_ref.controller is True
+ assert owner_ref.block_owner_deletion is True
+
+ # Verify labels were set
+ assert created_configmap.metadata.labels["ray.io/job-name"] == "test-job"
+ assert (
+ created_configmap.metadata.labels["app.kubernetes.io/managed-by"]
+ == "codeflare-sdk"
+ )
+ assert (
+ created_configmap.metadata.labels["app.kubernetes.io/component"]
+ == "rayjob-scripts"
+ )
+
+ assert "main.py" in created_configmap.data
+ assert "helper.py" in created_configmap.data
+ assert (
+ "Adding owner reference to ConfigMap 'test-job-scripts' with RayJob UID: unique-rayjob-uid-12345"
+ in caplog.text
+ )
+
+ finally:
+ os.chdir(original_cwd)
+
+
+def test_find_local_imports_syntax_error(mocker, auto_mock_setup):
+ """Test _find_local_imports handles syntax errors gracefully."""
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ # Invalid Python syntax
+ invalid_script_content = "import helper\ndef invalid_syntax("
+
+ mock_callback = mocker.Mock()
+
+ rayjob._find_local_imports(invalid_script_content, "test.py", mock_callback)
+ mock_callback.assert_not_called()
+
+
+def test_create_configmap_api_error_non_409(auto_mock_setup):
+ """Test _create_configmap_from_spec handles non-409 API errors."""
+ mock_api_instance = auto_mock_setup["k8s_api"]
+
+ # Configure to raise 500 error
+ mock_api_instance.create_namespaced_config_map.side_effect = ApiException(
+ status=500
+ )
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ configmap_spec = {
+ "apiVersion": "v1",
+ "kind": "ConfigMap",
+ "metadata": {"name": "test-scripts", "namespace": "test-namespace"},
+ "data": {"test.py": "print('test')"},
+ }
+
+ with pytest.raises(RuntimeError, match="Failed to create ConfigMap"):
+ rayjob._create_configmap_from_spec(configmap_spec)
+
+
+def test_update_existing_cluster_get_cluster_error(mocker, auto_mock_setup):
+ """Test _update_existing_cluster_for_scripts handles get cluster errors."""
+ mock_cluster_api_instance = auto_mock_setup["cluster_api"]
+
+ # Configure it to raise an error
+ mock_cluster_api_instance.get_ray_cluster.side_effect = ApiException(status=404)
+
+ config_builder = ManagedClusterConfig()
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ with pytest.raises(RuntimeError, match="Failed to get RayCluster"):
+ rayjob._update_existing_cluster_for_scripts("test-scripts", config_builder)
+
+
+def test_update_existing_cluster_patch_error(mocker, auto_mock_setup):
+ """Test _update_existing_cluster_for_scripts handles patch errors."""
+ mock_cluster_api_instance = auto_mock_setup["cluster_api"]
+
+ # Mock successful get but failed patch
+ mock_cluster_api_instance.get_ray_cluster.return_value = {
+ "spec": {
+ "headGroupSpec": {
+ "template": {
+ "spec": {"volumes": [], "containers": [{"volumeMounts": []}]}
+ }
+ },
+ "workerGroupSpecs": [
+ {
+ "template": {
+ "spec": {"volumes": [], "containers": [{"volumeMounts": []}]}
+ }
+ }
+ ],
+ }
+ }
+
+ mock_cluster_api_instance.patch_ray_cluster.side_effect = ApiException(status=500)
+
+ config_builder = ManagedClusterConfig()
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python test.py",
+ namespace="test-namespace",
+ )
+
+ with pytest.raises(RuntimeError, match="Failed to update RayCluster"):
+ rayjob._update_existing_cluster_for_scripts("test-scripts", config_builder)
+
+
+def test_extract_script_files_empty_entrypoint(auto_mock_setup):
+ """Test script extraction with empty entrypoint."""
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="", # Empty entrypoint
+ namespace="test-namespace",
+ )
+
+ scripts = rayjob._extract_script_files_from_entrypoint()
+
+ assert scripts is None
+
+
+def test_add_script_volumes_existing_volume_skip():
+ """Test add_script_volumes skips when volume already exists (missing coverage)."""
+ config = ManagedClusterConfig()
+
+ # Pre-add a volume with same name
+ existing_volume = V1Volume(
+ name="ray-job-scripts",
+ config_map=V1ConfigMapVolumeSource(name="existing-scripts"),
+ )
+ config.volumes.append(existing_volume)
+
+ config.add_script_volumes(configmap_name="new-scripts")
+ assert len(config.volumes) == 1
+ assert len(config.volume_mounts) == 0 # Mount not added due to volume skip
+
+
+def test_add_script_volumes_existing_mount_skip():
+ """Test add_script_volumes skips when mount already exists (missing coverage)."""
+ config = ManagedClusterConfig()
+
+ # Pre-add a mount with same name
+ existing_mount = V1VolumeMount(name="ray-job-scripts", mount_path="/existing/path")
+ config.volume_mounts.append(existing_mount)
+
+ config.add_script_volumes(configmap_name="new-scripts")
+ assert len(config.volumes) == 0 # Volume not added due to mount skip
+ assert len(config.volume_mounts) == 1
+
+
+def test_rayjob_stop_success(auto_mock_setup, caplog):
+ """Test successful RayJob stop operation."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+
+ mock_api_instance.suspend_job.return_value = {
+ "metadata": {"name": "test-rayjob"},
+ "spec": {"suspend": True},
+ }
+
+ rayjob = RayJob(
+ job_name="test-rayjob",
+ cluster_name="test-cluster",
+ namespace="test-namespace",
+ entrypoint="python script.py",
+ )
+
+ with caplog.at_level("INFO"):
+ result = rayjob.stop()
+
+ assert result is True
+
+ mock_api_instance.suspend_job.assert_called_once_with(
+ name="test-rayjob", k8s_namespace="test-namespace"
+ )
+
+ # Verify success message was logged
+ assert "Successfully stopped the RayJob test-rayjob" in caplog.text
+
+
+def test_rayjob_stop_failure(auto_mock_setup):
+ """Test RayJob stop operation when API call fails."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+
+ mock_api_instance.suspend_job.return_value = None
+
+ rayjob = RayJob(
+ job_name="test-rayjob",
+ cluster_name="test-cluster",
+ namespace="test-namespace",
+ entrypoint="python script.py",
+ )
+
+ with pytest.raises(RuntimeError, match="Failed to stop the RayJob test-rayjob"):
+ rayjob.stop()
+
+ mock_api_instance.suspend_job.assert_called_once_with(
+ name="test-rayjob", k8s_namespace="test-namespace"
+ )
+
+
+def test_rayjob_resubmit_success(auto_mock_setup):
+ """Test successful RayJob resubmit operation."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+
+ mock_api_instance.resubmit_job.return_value = {
+ "metadata": {"name": "test-rayjob"},
+ "spec": {"suspend": False},
+ }
+
+ rayjob = RayJob(
+ job_name="test-rayjob",
+ cluster_name="test-cluster",
+ namespace="test-namespace",
+ entrypoint="python script.py",
+ )
+
+ result = rayjob.resubmit()
+
+ assert result is True
+
+ mock_api_instance.resubmit_job.assert_called_once_with(
+ name="test-rayjob", k8s_namespace="test-namespace"
+ )
+
+
+def test_rayjob_resubmit_failure(auto_mock_setup):
+ """Test RayJob resubmit operation when API call fails."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+
+ mock_api_instance.resubmit_job.return_value = None
+
+ rayjob = RayJob(
+ job_name="test-rayjob",
+ cluster_name="test-cluster",
+ namespace="test-namespace",
+ entrypoint="python script.py",
+ )
+
+ with pytest.raises(RuntimeError, match="Failed to resubmit the RayJob test-rayjob"):
+ rayjob.resubmit()
+
+ mock_api_instance.resubmit_job.assert_called_once_with(
+ name="test-rayjob", k8s_namespace="test-namespace"
+ )
+
+
+def test_rayjob_delete_success(auto_mock_setup):
+ """Test successful RayJob deletion."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+
+ rayjob = RayJob(
+ job_name="test-rayjob",
+ entrypoint="python script.py",
+ cluster_name="test-cluster",
+ )
+
+ mock_api_instance.delete_job.return_value = True
+
+ result = rayjob.delete()
+
+ assert result is True
+ mock_api_instance.delete_job.assert_called_once_with(
+ name="test-rayjob", k8s_namespace="test-namespace"
+ )
+
+
+def test_rayjob_delete_failure(auto_mock_setup):
+ """Test failed RayJob deletion."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+
+ rayjob = RayJob(
+ job_name="test-rayjob",
+ entrypoint="python script.py",
+ cluster_name="test-cluster",
+ )
+
+ mock_api_instance.delete_job.return_value = False
+
+ with pytest.raises(RuntimeError, match="Failed to delete the RayJob test-rayjob"):
+ rayjob.delete()
+
+ mock_api_instance.delete_job.assert_called_once_with(
+ name="test-rayjob", k8s_namespace="test-namespace"
+ )
+
+
+def test_rayjob_init_both_none_error(auto_mock_setup):
+ """Test RayJob initialization error when both cluster_name and cluster_config are None."""
+ with pytest.raises(
+ ValueError,
+ match="Configuration Error: You must provide either 'cluster_name' .* or 'cluster_config'",
+ ):
+ RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_name=None,
+ cluster_config=None,
+ )
+
+
+def test_rayjob_init_missing_cluster_name_with_no_config(auto_mock_setup):
+ """Test RayJob initialization error when cluster_name is None without cluster_config."""
+ with pytest.raises(
+ ValueError,
+ match="Configuration Error: a 'cluster_name' is required when not providing 'cluster_config'",
+ ):
+ rayjob = RayJob.__new__(RayJob)
+ rayjob.name = "test-job"
+ rayjob.entrypoint = "python script.py"
+ rayjob.runtime_env = None
+ rayjob.ttl_seconds_after_finished = 0
+ rayjob.active_deadline_seconds = None
+ rayjob.shutdown_after_job_finishes = True
+ rayjob.namespace = "test-namespace"
+ rayjob._cluster_name = None
+ rayjob._cluster_config = None
+ if rayjob._cluster_config is None and rayjob._cluster_name is None:
+ raise ValueError(
+ "❌ Configuration Error: a 'cluster_name' is required when not providing 'cluster_config'"
+ )
+
+
+def test_handle_script_volumes_for_existing_cluster_direct_call(auto_mock_setup):
+ """Test _handle_script_volumes_for_existing_cluster method directly."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+ mock_cluster_api = auto_mock_setup["cluster_api"]
+ mock_k8s_api = auto_mock_setup["k8s_api"]
+
+ # Mock existing cluster
+ mock_cluster = {
+ "spec": {
+ "headGroupSpec": {
+ "template": {
+ "spec": {"containers": [{"volumeMounts": []}], "volumes": []}
+ }
+ },
+ "workerGroupSpecs": [
+ {
+ "template": {
+ "spec": {"containers": [{"volumeMounts": []}], "volumes": []}
+ }
+ }
+ ],
+ }
+ }
+ mock_cluster_api.get_ray_cluster.return_value = mock_cluster
+
+ rayjob = RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_name="existing-cluster",
+ )
+
+ scripts = {"test_script.py": "print('Hello World')"}
+ rayjob._handle_script_volumes_for_existing_cluster(
+ scripts, {"metadata": {"uid": "test-uid"}}
+ )
+
+ mock_k8s_api.create_namespaced_config_map.assert_called_once()
+ created_configmap = mock_k8s_api.create_namespaced_config_map.call_args[1]["body"]
+ assert "test_script.py" in created_configmap.data
+
+ mock_cluster_api.patch_ray_cluster.assert_called_once_with(
+ name="existing-cluster", ray_patch=mock_cluster, k8s_namespace="test-namespace"
+ )
+
+
+def test_handle_script_volumes_for_existing_cluster_no_volumes_init(auto_mock_setup):
+ """Test _handle_script_volumes_for_existing_cluster when volumes/mounts don't exist initially."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+ mock_cluster_api = auto_mock_setup["cluster_api"]
+ mock_k8s_api = auto_mock_setup["k8s_api"]
+
+ # Mock existing cluster WITHOUT volumes/volumeMounts (to test initialization)
+ mock_cluster = {
+ "spec": {
+ "headGroupSpec": {"template": {"spec": {"containers": [{}]}}},
+ "workerGroupSpecs": [{"template": {"spec": {"containers": [{}]}}}],
+ }
+ }
+ mock_cluster_api.get_ray_cluster.return_value = mock_cluster
+
+ # Create RayJob with existing cluster
+ rayjob = RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_name="existing-cluster",
+ )
+
+ # Call the method directly with test scripts
+ scripts = {"test_script.py": "print('Hello World')"}
+ rayjob._handle_script_volumes_for_existing_cluster(
+ scripts, {"metadata": {"uid": "test-uid"}}
+ )
+
+ # Verify volumes and volumeMounts were initialized
+ patched_cluster = mock_cluster_api.patch_ray_cluster.call_args[1]["ray_patch"]
+
+ # Check head group
+ head_spec = patched_cluster["spec"]["headGroupSpec"]["template"]["spec"]
+ assert "volumes" in head_spec
+ assert len(head_spec["volumes"]) == 1
+ assert "volumeMounts" in head_spec["containers"][0]
+ assert len(head_spec["containers"][0]["volumeMounts"]) == 1
+
+ # Check worker group
+ worker_spec = patched_cluster["spec"]["workerGroupSpecs"][0]["template"]["spec"]
+ assert "volumes" in worker_spec
+ assert len(worker_spec["volumes"]) == 1
+ assert "volumeMounts" in worker_spec["containers"][0]
+ assert len(worker_spec["containers"][0]["volumeMounts"]) == 1
+
+
+def test_update_existing_cluster_for_scripts_api_errors(mocker, auto_mock_setup):
+ """Test _update_existing_cluster_for_scripts error handling."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+ mock_cluster_api = auto_mock_setup["cluster_api"]
+
+ # Mock config builder
+ mock_config_builder = mocker.MagicMock()
+ mocker.patch(
+ "codeflare_sdk.ray.rayjobs.rayjob.ManagedClusterConfig",
+ return_value=mock_config_builder,
+ )
+
+ # Set up config builder to return valid specs
+ mock_config_builder.build_script_volume_specs.return_value = (
+ {"name": "script-volume", "configMap": {"name": "test-configmap"}},
+ {"name": "script-volume", "mountPath": "/home/ray/scripts"},
+ )
+
+ # Mock cluster API to raise error
+ mock_cluster_api.get_ray_cluster.side_effect = ApiException(
+ status=404, reason="Not Found"
+ )
+
+ # Create RayJob
+ rayjob = RayJob(
+ job_name="test-job",
+ entrypoint="python script.py",
+ cluster_name="existing-cluster",
+ )
+
+ # Call the method directly
+ with pytest.raises(
+ RuntimeError, match="Failed to get RayCluster 'existing-cluster'"
+ ):
+ rayjob._update_existing_cluster_for_scripts(
+ "test-configmap", mock_config_builder
+ )
+
+
+def test_rayjob_kueue_label_no_default_queue(auto_mock_setup, mocker, caplog):
+ """Test RayJob falls back to 'default' queue when no default queue exists."""
+ mocker.patch(
+ "codeflare_sdk.ray.rayjobs.rayjob.get_default_kueue_name",
+ return_value=None,
+ )
+
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+ mock_api_instance.submit_job.return_value = {"metadata": {"name": "test-job"}}
+
+ cluster_config = ManagedClusterConfig()
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python script.py",
+ )
+
+ with caplog.at_level("WARNING"):
+ rayjob.submit()
+
+ # Verify the submitted job has the fallback label
+ call_args = mock_api_instance.submit_job.call_args
+ submitted_job = call_args.kwargs["job"]
+ assert submitted_job["metadata"]["labels"]["kueue.x-k8s.io/queue-name"] == "default"
+
+ # Verify warning was logged
+ assert "No default Kueue LocalQueue found" in caplog.text
+
+
+def test_rayjob_kueue_explicit_local_queue(auto_mock_setup):
+ """Test RayJob uses explicitly specified local queue."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+ mock_api_instance.submit_job.return_value = {"metadata": {"name": "test-job"}}
+
+ cluster_config = ManagedClusterConfig()
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python script.py",
+ local_queue="custom-queue",
+ )
+
+ rayjob.submit()
+
+ # Verify the submitted job has the explicit queue label
+ call_args = mock_api_instance.submit_job.call_args
+ submitted_job = call_args.kwargs["job"]
+ assert (
+ submitted_job["metadata"]["labels"]["kueue.x-k8s.io/queue-name"]
+ == "custom-queue"
+ )
+
+
+def test_rayjob_no_kueue_label_for_existing_cluster(auto_mock_setup):
+ """Test RayJob doesn't add Kueue label for existing clusters."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+ mock_api_instance.submit_job.return_value = {"metadata": {"name": "test-job"}}
+
+ # Using existing cluster (no cluster_config)
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="existing-cluster",
+ entrypoint="python script.py",
+ )
+
+ rayjob.submit()
+
+ # Verify no Kueue label was added
+ call_args = mock_api_instance.submit_job.call_args
+ submitted_job = call_args.kwargs["job"]
+ assert "kueue.x-k8s.io/queue-name" not in submitted_job["metadata"]["labels"]
+
+
+def test_rayjob_with_ttl_and_deadline(auto_mock_setup):
+ """Test RayJob with TTL and active deadline seconds."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+ mock_api_instance.submit_job.return_value = {"metadata": {"name": "test-job"}}
+
+ cluster_config = ManagedClusterConfig()
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python script.py",
+ ttl_seconds_after_finished=300,
+ active_deadline_seconds=600,
+ )
+
+ rayjob.submit()
+
+ # Verify TTL and deadline were set
+ call_args = mock_api_instance.submit_job.call_args
+ submitted_job = call_args.kwargs["job"]
+ assert submitted_job["spec"]["ttlSecondsAfterFinished"] == 300
+ assert submitted_job["spec"]["activeDeadlineSeconds"] == 600
+
+
+def test_rayjob_shutdown_after_job_finishes(auto_mock_setup):
+ """Test RayJob sets shutdownAfterJobFinishes correctly."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+ mock_api_instance.submit_job.return_value = {"metadata": {"name": "test-job"}}
+
+ # Test with managed cluster (should shutdown)
+ cluster_config = ManagedClusterConfig()
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python script.py",
+ )
+
+ rayjob.submit()
+
+ call_args = mock_api_instance.submit_job.call_args
+ submitted_job = call_args.kwargs["job"]
+ assert submitted_job["spec"]["shutdownAfterJobFinishes"] is True
+
+ # Test with existing cluster (should not shutdown)
+ rayjob2 = RayJob(
+ job_name="test-job2",
+ cluster_name="existing-cluster",
+ entrypoint="python script.py",
+ )
+
+ rayjob2.submit()
+
+ call_args2 = mock_api_instance.submit_job.call_args
+ submitted_job2 = call_args2.kwargs["job"]
+ assert submitted_job2["spec"]["shutdownAfterJobFinishes"] is False
+
+
+def test_rayjob_stop_delete_resubmit_logging(auto_mock_setup, caplog):
+ """Test logging for stop, delete, and resubmit operations."""
+ mock_api_instance = auto_mock_setup["rayjob_api"]
+
+ # Test stop with logging
+ mock_api_instance.suspend_job.return_value = {
+ "metadata": {"name": "test-rayjob"},
+ "spec": {"suspend": True},
+ }
+
+ rayjob = RayJob(
+ job_name="test-rayjob",
+ cluster_name="test-cluster",
+ namespace="test-namespace",
+ entrypoint="python script.py",
+ )
+
+ with caplog.at_level("INFO"):
+ result = rayjob.stop()
+
+ assert result is True
+ assert "Successfully stopped the RayJob test-rayjob" in caplog.text
+
+ # Test delete with logging
+ caplog.clear()
+ mock_api_instance.delete_job.return_value = True
+
+ with caplog.at_level("INFO"):
+ result = rayjob.delete()
+
+ assert result is True
+ assert "Successfully deleted the RayJob test-rayjob" in caplog.text
+
+ # Test resubmit with logging
+ caplog.clear()
+ mock_api_instance.resubmit_job.return_value = {
+ "metadata": {"name": "test-rayjob"},
+ "spec": {"suspend": False},
+ }
+
+ with caplog.at_level("INFO"):
+ result = rayjob.resubmit()
+
+ assert result is True
+ assert "Successfully resubmitted the RayJob test-rayjob" in caplog.text
+
+
+def test_rayjob_initialization_logging(auto_mock_setup, caplog):
+ """Test RayJob initialization logging."""
+ with caplog.at_level("INFO"):
+ cluster_config = ManagedClusterConfig()
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_config=cluster_config,
+ entrypoint="python script.py",
+ )
+
+ assert "Creating new cluster: test-job-cluster" in caplog.text
+ assert "Initialized RayJob: test-job in namespace: test-namespace" in caplog.text
diff --git a/src/codeflare_sdk/ray/rayjobs/test_status.py b/src/codeflare_sdk/ray/rayjobs/test_status.py
new file mode 100644
index 00000000..2f2b9957
--- /dev/null
+++ b/src/codeflare_sdk/ray/rayjobs/test_status.py
@@ -0,0 +1,373 @@
+# Copyright 2025 IBM, Red Hat
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from codeflare_sdk.ray.rayjobs.rayjob import RayJob
+from codeflare_sdk.ray.rayjobs.status import (
+ CodeflareRayJobStatus,
+ RayJobDeploymentStatus,
+ RayJobInfo,
+)
+
+
+def test_rayjob_status(mocker):
+ """
+ Test the RayJob status method with different deployment statuses.
+ """
+ # Mock kubernetes config loading
+ mocker.patch("kubernetes.config.load_kube_config")
+ # Mock the RayjobApi to avoid actual Kubernetes calls
+ mock_api_class = mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayjobApi")
+ mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayClusterApi")
+ mock_api_instance = mock_api_class.return_value
+
+ # Create a RayJob instance
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="test-cluster",
+ namespace="test-ns",
+ entrypoint="python test.py",
+ )
+
+ # Test case 1: No job found
+ mock_api_instance.get_job_status.return_value = None
+ status, ready = rayjob.status(print_to_console=False)
+ assert status == CodeflareRayJobStatus.UNKNOWN
+ assert ready == False
+
+ # Test case 2: Running job
+ mock_api_instance.get_job_status.return_value = {
+ "jobId": "test-job-abc123",
+ "jobDeploymentStatus": "Running",
+ "startTime": "2025-07-28T11:37:07Z",
+ "failed": 0,
+ "succeeded": 0,
+ "rayClusterName": "test-cluster",
+ }
+ status, ready = rayjob.status(print_to_console=False)
+ assert status == CodeflareRayJobStatus.RUNNING
+ assert ready == False
+
+ # Test case 3: Complete job
+ mock_api_instance.get_job_status.return_value = {
+ "jobId": "test-job-abc123",
+ "jobDeploymentStatus": "Complete",
+ "startTime": "2025-07-28T11:37:07Z",
+ "endTime": "2025-07-28T11:42:30Z",
+ "failed": 0,
+ "succeeded": 1,
+ "rayClusterName": "test-cluster",
+ }
+ status, ready = rayjob.status(print_to_console=False)
+ assert status == CodeflareRayJobStatus.COMPLETE
+ assert ready == True
+
+ # Test case 4: Failed job
+ mock_api_instance.get_job_status.return_value = {
+ "jobId": "test-job-abc123",
+ "jobDeploymentStatus": "Failed",
+ "startTime": "2025-07-28T11:37:07Z",
+ "endTime": "2025-07-28T11:42:30Z",
+ "failed": 1,
+ "succeeded": 0,
+ "rayClusterName": "test-cluster",
+ }
+ status, ready = rayjob.status(print_to_console=False)
+ assert status == CodeflareRayJobStatus.FAILED
+ assert ready == False
+
+ # Test case 5: Suspended job
+ mock_api_instance.get_job_status.return_value = {
+ "jobId": "test-job-abc123",
+ "jobDeploymentStatus": "Suspended",
+ "startTime": "2025-07-28T11:37:07Z",
+ "failed": 0,
+ "succeeded": 0,
+ "rayClusterName": "test-cluster",
+ }
+ status, ready = rayjob.status(print_to_console=False)
+ assert status == CodeflareRayJobStatus.SUSPENDED
+ assert ready == False
+
+
+def test_rayjob_status_unknown_deployment_status(mocker):
+ """
+ Test handling of unknown deployment status from the API.
+ """
+ mocker.patch("kubernetes.config.load_kube_config")
+ mock_api_class = mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayjobApi")
+ mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayClusterApi")
+ mock_api_instance = mock_api_class.return_value
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="test-cluster",
+ namespace="test-ns",
+ entrypoint="python test.py",
+ )
+
+ # Test with unrecognized deployment status
+ mock_api_instance.get_job_status.return_value = {
+ "jobId": "test-job-abc123",
+ "jobDeploymentStatus": "SomeNewStatus", # Unknown status
+ "startTime": "2025-07-28T11:37:07Z",
+ "failed": 0,
+ "succeeded": 0,
+ }
+
+ status, ready = rayjob.status(print_to_console=False)
+ assert status == CodeflareRayJobStatus.UNKNOWN
+ assert ready == False
+
+
+def test_rayjob_status_missing_fields(mocker):
+ """
+ Test handling of API response with missing fields.
+ """
+ mocker.patch("kubernetes.config.load_kube_config")
+ mock_api_class = mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayjobApi")
+ mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayClusterApi")
+ mock_api_instance = mock_api_class.return_value
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="test-cluster",
+ namespace="test-ns",
+ entrypoint="python test.py",
+ )
+
+ # Test with minimal API response (missing some fields)
+ mock_api_instance.get_job_status.return_value = {
+ # Missing jobId, failed, succeeded, etc.
+ "jobDeploymentStatus": "Running",
+ }
+
+ status, ready = rayjob.status(print_to_console=False)
+ assert status == CodeflareRayJobStatus.RUNNING
+ assert ready == False
+
+
+def test_map_to_codeflare_status(mocker):
+ """
+ Test the _map_to_codeflare_status helper method directly.
+ """
+ # Mock kubernetes config loading
+ mocker.patch("kubernetes.config.load_kube_config")
+ # Mock the RayjobApi constructor to avoid authentication issues
+ mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayjobApi")
+ mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayClusterApi")
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="test-cluster",
+ namespace="test-ns",
+ entrypoint="python test.py",
+ )
+
+ # Test all deployment status mappings
+ status, ready = rayjob._map_to_codeflare_status(RayJobDeploymentStatus.COMPLETE)
+ assert status == CodeflareRayJobStatus.COMPLETE
+ assert ready == True
+
+ status, ready = rayjob._map_to_codeflare_status(RayJobDeploymentStatus.RUNNING)
+ assert status == CodeflareRayJobStatus.RUNNING
+ assert ready == False
+
+ status, ready = rayjob._map_to_codeflare_status(RayJobDeploymentStatus.FAILED)
+ assert status == CodeflareRayJobStatus.FAILED
+ assert ready == False
+
+ status, ready = rayjob._map_to_codeflare_status(RayJobDeploymentStatus.SUSPENDED)
+ assert status == CodeflareRayJobStatus.SUSPENDED
+ assert ready == False
+
+ status, ready = rayjob._map_to_codeflare_status(RayJobDeploymentStatus.UNKNOWN)
+ assert status == CodeflareRayJobStatus.UNKNOWN
+ assert ready == False
+
+
+def test_rayjob_info_dataclass():
+ """
+ Test the RayJobInfo dataclass creation and field access.
+ """
+ job_info = RayJobInfo(
+ name="test-job",
+ job_id="test-job-abc123",
+ status=RayJobDeploymentStatus.RUNNING,
+ namespace="test-ns",
+ cluster_name="test-cluster",
+ start_time="2025-07-28T11:37:07Z",
+ failed_attempts=0,
+ succeeded_attempts=0,
+ )
+
+ # Test all fields are accessible
+ assert job_info.name == "test-job"
+ assert job_info.job_id == "test-job-abc123"
+ assert job_info.status == RayJobDeploymentStatus.RUNNING
+ assert job_info.namespace == "test-ns"
+ assert job_info.cluster_name == "test-cluster"
+ assert job_info.start_time == "2025-07-28T11:37:07Z"
+ assert job_info.end_time is None # Default value
+ assert job_info.failed_attempts == 0
+ assert job_info.succeeded_attempts == 0
+
+
+def test_rayjob_status_print_no_job_found(mocker):
+ """
+ Test that pretty_print.print_no_job_found is called when no job is found and print_to_console=True.
+ """
+ mocker.patch("kubernetes.config.load_kube_config")
+ # Mock the RayjobApi and pretty_print
+ mock_api_class = mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayjobApi")
+ mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayClusterApi")
+ mock_api_instance = mock_api_class.return_value
+ mock_print_no_job_found = mocker.patch(
+ "codeflare_sdk.ray.rayjobs.pretty_print.print_no_job_found"
+ )
+
+ # Create a RayJob instance
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="test-cluster",
+ namespace="test-ns",
+ entrypoint="python test.py",
+ )
+
+ # No job found scenario
+ mock_api_instance.get_job_status.return_value = None
+
+ # Call status with print_to_console=True
+ status, ready = rayjob.status(print_to_console=True)
+
+ # Verify the pretty print function was called
+ mock_print_no_job_found.assert_called_once_with("test-job", "test-ns")
+ assert status == CodeflareRayJobStatus.UNKNOWN
+ assert ready == False
+
+
+def test_rayjob_status_print_job_found(mocker):
+ """
+ Test that pretty_print.print_job_status is called when job is found and print_to_console=True.
+ """
+ mocker.patch("kubernetes.config.load_kube_config")
+ # Mock the RayjobApi and pretty_print
+ mock_api_class = mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayjobApi")
+ mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayClusterApi")
+ mock_api_instance = mock_api_class.return_value
+ mock_print_job_status = mocker.patch(
+ "codeflare_sdk.ray.rayjobs.pretty_print.print_job_status"
+ )
+
+ # Create a RayJob instance
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="test-cluster",
+ namespace="test-ns",
+ entrypoint="python test.py",
+ )
+
+ # Job found scenario
+ mock_api_instance.get_job_status.return_value = {
+ "jobId": "test-job-abc123",
+ "jobDeploymentStatus": "Running",
+ "startTime": "2025-07-28T11:37:07Z",
+ "failed": 0,
+ "succeeded": 0,
+ "rayClusterName": "test-cluster",
+ }
+
+ # Call status with print_to_console=True
+ status, ready = rayjob.status(print_to_console=True)
+
+ # Verify the pretty print function was called
+ mock_print_job_status.assert_called_once()
+ # Verify the RayJobInfo object passed to print_job_status
+ call_args = mock_print_job_status.call_args[0][0] # First positional argument
+ assert call_args.name == "test-job"
+ assert call_args.job_id == "test-job-abc123"
+ assert call_args.status == RayJobDeploymentStatus.RUNNING
+ assert call_args.namespace == "test-ns"
+ assert call_args.cluster_name == "test-cluster"
+
+ assert status == CodeflareRayJobStatus.RUNNING
+ assert ready == False
+
+
+def test_rayjob_status_all_deployment_states(mocker):
+ """Test RayJob status method with all deployment states."""
+ mocker.patch("kubernetes.config.load_kube_config")
+ mock_api_class = mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayjobApi")
+ mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayClusterApi")
+ mock_api_instance = mock_api_class.return_value
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="test-cluster",
+ namespace="test-ns",
+ entrypoint="python test.py",
+ )
+
+ # Test mapping of deployment statuses to CodeflareRayJobStatus
+ test_cases = [
+ # (deployment_status_str, expected CodeflareRayJobStatus, expected ready)
+ ("Complete", CodeflareRayJobStatus.COMPLETE, True),
+ ("Running", CodeflareRayJobStatus.RUNNING, False),
+ ("Failed", CodeflareRayJobStatus.FAILED, False),
+ ("Suspended", CodeflareRayJobStatus.SUSPENDED, False),
+ ]
+
+ for deployment_status_str, expected_status, expected_ready in test_cases:
+ mock_api_instance.get_job_status.return_value = {
+ "jobId": "test-job-abc123",
+ "jobDeploymentStatus": deployment_status_str,
+ "startTime": "2025-07-28T11:37:07Z",
+ "failed": 0,
+ "succeeded": 0,
+ "rayClusterName": "test-cluster",
+ }
+ status, ready = rayjob.status(print_to_console=False)
+ assert status == expected_status, f"Failed for {deployment_status_str}"
+ assert (
+ ready == expected_ready
+ ), f"Failed ready check for {deployment_status_str}"
+
+
+def test_rayjob_status_with_end_time(mocker):
+ """Test RayJob status with end time field."""
+ mocker.patch("kubernetes.config.load_kube_config")
+ mock_api_class = mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayjobApi")
+ mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayClusterApi")
+ mock_api_instance = mock_api_class.return_value
+
+ rayjob = RayJob(
+ job_name="test-job",
+ cluster_name="test-cluster",
+ namespace="test-ns",
+ entrypoint="python test.py",
+ )
+
+ # Test with end time field
+ mock_api_instance.get_job_status.return_value = {
+ "jobId": "test-job-abc123",
+ "jobDeploymentStatus": "Complete",
+ "startTime": "2025-07-28T11:37:07Z",
+ "endTime": "2025-07-28T11:47:07Z",
+ "failed": 0,
+ "succeeded": 1,
+ "rayClusterName": "test-cluster",
+ }
+
+ status, ready = rayjob.status(print_to_console=False)
+ assert status == CodeflareRayJobStatus.COMPLETE
+ assert ready == True
diff --git a/tests/e2e/cluster_apply_kind_test.py b/tests/e2e/cluster_apply_kind_test.py
index 398bf73b..e023e92d 100644
--- a/tests/e2e/cluster_apply_kind_test.py
+++ b/tests/e2e/cluster_apply_kind_test.py
@@ -1,6 +1,8 @@
from codeflare_sdk import Cluster, ClusterConfiguration
import pytest
+import time
from kubernetes import client
+from codeflare_sdk.common.utils import constants
from support import (
initialize_kubernetes_client,
@@ -38,6 +40,7 @@ def test_cluster_apply(self):
worker_cpu_limits="1",
worker_memory_requests="1Gi",
worker_memory_limits="2Gi",
+ image=f"rayproject/ray:{constants.RAY_VERSION}",
write_to_file=True,
verify_tls=False,
)
@@ -47,9 +50,9 @@ def test_cluster_apply(self):
cluster.apply()
# Wait for the cluster to be ready
- cluster.wait_ready()
- status = cluster.status()
- assert status["ready"], f"Cluster {cluster_name} is not ready: {status}"
+ cluster.wait_ready(dashboard_check=False)
+ status, ready = cluster.status()
+ assert ready, f"Cluster {cluster_name} is not ready: {status}"
# Verify the cluster is created
ray_cluster = get_ray_cluster(cluster_name, namespace)
@@ -58,7 +61,7 @@ def test_cluster_apply(self):
ray_cluster["spec"]["workerGroupSpecs"][0]["replicas"] == 1
), "Initial worker count does not match"
- # Update configuration with 3 workers
+ # Update configuration with 2 workers
updated_config = ClusterConfiguration(
name=cluster_name,
namespace=namespace,
@@ -71,6 +74,7 @@ def test_cluster_apply(self):
worker_cpu_limits="1",
worker_memory_requests="1Gi",
worker_memory_limits="2Gi",
+ image=f"rayproject/ray:{constants.RAY_VERSION}",
write_to_file=True,
verify_tls=False,
)
@@ -79,12 +83,15 @@ def test_cluster_apply(self):
cluster.config = updated_config
cluster.apply()
+ # Give Kubernetes a moment to process the update
+ time.sleep(5)
+
# Wait for the updated cluster to be ready
- cluster.wait_ready()
- updated_status = cluster.status()
- assert updated_status[
- "ready"
- ], f"Cluster {cluster_name} is not ready after update: {updated_status}"
+ cluster.wait_ready(dashboard_check=False)
+ updated_status, updated_ready = cluster.status()
+ assert (
+ updated_ready
+ ), f"Cluster {cluster_name} is not ready after update: {updated_status}"
# Verify the cluster is updated
updated_ray_cluster = get_ray_cluster(cluster_name, namespace)
@@ -94,63 +101,19 @@ def test_cluster_apply(self):
# Clean up
cluster.down()
- ray_cluster = get_ray_cluster(cluster_name, namespace)
- assert ray_cluster is None, "Cluster was not deleted successfully"
- def test_apply_invalid_update(self):
- self.setup_method()
- create_namespace(self)
+ # Wait for deletion to complete (finalizers may delay deletion)
+ max_wait = 30 # seconds
+ wait_interval = 2
+ elapsed = 0
- cluster_name = "test-cluster-apply-invalid"
- namespace = self.namespace
+ while elapsed < max_wait:
+ ray_cluster = get_ray_cluster(cluster_name, namespace)
+ if ray_cluster is None:
+ break
+ time.sleep(wait_interval)
+ elapsed += wait_interval
- # Initial configuration
- initial_config = ClusterConfiguration(
- name=cluster_name,
- namespace=namespace,
- num_workers=1,
- head_cpu_requests="500m",
- head_cpu_limits="1",
- head_memory_requests="1Gi",
- head_memory_limits="2Gi",
- worker_cpu_requests="500m",
- worker_cpu_limits="1",
- worker_memory_requests="1Gi",
- worker_memory_limits="2Gi",
- write_to_file=True,
- verify_tls=False,
- )
-
- # Create the cluster
- cluster = Cluster(initial_config)
- cluster.apply()
-
- # Wait for the cluster to be ready
- cluster.wait_ready()
- status = cluster.status()
- assert status["ready"], f"Cluster {cluster_name} is not ready: {status}"
-
- # Update with an invalid configuration (e.g., immutable field change)
- invalid_config = ClusterConfiguration(
- name=cluster_name,
- namespace=namespace,
- num_workers=2,
- head_cpu_requests="1",
- head_cpu_limits="2", # Changing CPU limits (immutable)
- head_memory_requests="1Gi",
- head_memory_limits="2Gi",
- worker_cpu_requests="500m",
- worker_cpu_limits="1",
- worker_memory_requests="1Gi",
- worker_memory_limits="2Gi",
- write_to_file=True,
- verify_tls=False,
- )
-
- # Try to apply the invalid configuration and expect failure
- cluster.config = invalid_config
- with pytest.raises(RuntimeError, match="Immutable fields detected"):
- cluster.apply()
-
- # Clean up
- cluster.down()
+ assert (
+ ray_cluster is None
+ ), f"Cluster was not deleted successfully after {max_wait}s"
diff --git a/tests/e2e/rayjob/__init__.py b/tests/e2e/rayjob/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/e2e/rayjob/ray_version_validation_oauth_test.py b/tests/e2e/rayjob/ray_version_validation_oauth_test.py
new file mode 100644
index 00000000..794d739a
--- /dev/null
+++ b/tests/e2e/rayjob/ray_version_validation_oauth_test.py
@@ -0,0 +1,127 @@
+import pytest
+import sys
+import os
+
+# Add the parent directory to the path to import support
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+from support import *
+
+from codeflare_sdk import (
+ RayJob,
+ ManagedClusterConfig,
+)
+
+
+class TestRayJobRayVersionValidationOauth:
+ def setup_method(self):
+ initialize_kubernetes_client(self)
+
+ def teardown_method(self):
+ delete_namespace(self)
+ delete_kueue_resources(self)
+
+ def _create_basic_managed_cluster_config(
+ self, ray_image: str
+ ) -> ManagedClusterConfig:
+ """Helper method to create basic managed cluster configuration."""
+ return ManagedClusterConfig(
+ head_cpu_requests="500m",
+ head_cpu_limits="500m",
+ head_memory_requests=1,
+ head_memory_limits=2,
+ num_workers=1,
+ worker_cpu_requests="500m",
+ worker_cpu_limits="500m",
+ worker_memory_requests=1,
+ worker_memory_limits=2,
+ image=ray_image,
+ )
+
+ def test_rayjob_lifecycled_cluster_incompatible_ray_version_oauth(self):
+ """Test that RayJob creation fails when cluster config specifies incompatible Ray version."""
+ self.setup_method()
+ create_namespace(self)
+ create_kueue_resources(self)
+ self.run_rayjob_lifecycled_cluster_incompatible_version()
+
+ def run_rayjob_lifecycled_cluster_incompatible_version(self):
+ """Test Ray version validation with cluster lifecycling using incompatible image."""
+
+ job_name = "incompatible-lifecycle-rayjob"
+
+ # Create cluster configuration with incompatible Ray version (2.46.1 instead of expected 2.47.1)
+ incompatible_ray_image = "quay.io/modh/ray:2.46.1-py311-cu121"
+
+ print(
+ f"Creating RayJob with incompatible Ray image in cluster config: {incompatible_ray_image}"
+ )
+
+ cluster_config = self._create_basic_managed_cluster_config(
+ incompatible_ray_image
+ )
+
+ # Create RayJob with incompatible cluster config - this should fail during submission
+ rayjob = RayJob(
+ job_name=job_name,
+ cluster_config=cluster_config,
+ namespace=self.namespace,
+ entrypoint="python -c 'print(\"This should not run due to version mismatch\")'",
+ ttl_seconds_after_finished=30,
+ )
+
+ print(
+ f"Attempting to submit RayJob '{job_name}' with incompatible Ray version..."
+ )
+
+ # This should fail during submission due to Ray version validation
+ with pytest.raises(ValueError, match="Ray version mismatch detected"):
+ rayjob.submit()
+
+ print(
+ "✅ Ray version validation correctly prevented RayJob submission with incompatible cluster config!"
+ )
+
+ def test_rayjob_lifecycled_cluster_unknown_ray_version_oauth(self):
+ """Test that RayJob creation succeeds with warning when Ray version cannot be determined."""
+ self.setup_method()
+ create_namespace(self)
+ create_kueue_resources(self)
+ self.run_rayjob_lifecycled_cluster_unknown_version()
+
+ def run_rayjob_lifecycled_cluster_unknown_version(self):
+ """Test Ray version validation with unknown image (should warn but not fail)."""
+
+ job_name = "unknown-version-rayjob"
+
+ # Use an image where Ray version cannot be determined (SHA digest)
+ unknown_ray_image = "quay.io/modh/ray@sha256:6d076aeb38ab3c34a6a2ef0f58dc667089aa15826fa08a73273c629333e12f1e"
+
+ print(
+ f"Creating RayJob with image where Ray version cannot be determined: {unknown_ray_image}"
+ )
+
+ cluster_config = self._create_basic_managed_cluster_config(unknown_ray_image)
+
+ # Create RayJob with unknown version image - this should succeed with warning
+ rayjob = RayJob(
+ job_name=job_name,
+ cluster_config=cluster_config,
+ namespace=self.namespace,
+ entrypoint="python -c 'print(\"Testing unknown Ray version scenario\")'",
+ ttl_seconds_after_finished=30,
+ )
+
+ print(f"Attempting to submit RayJob '{job_name}' with unknown Ray version...")
+
+ # This should succeed but with a warning
+ with pytest.warns(UserWarning, match="Cannot determine Ray version"):
+ submission_result = rayjob.submit()
+
+ assert (
+ submission_result == job_name
+ ), f"Job submission failed, expected {job_name}, got {submission_result}"
+
+ print("✅ RayJob submission succeeded with warning for unknown Ray version!")
+ print(
+ f"Note: RayJob '{job_name}' was submitted successfully but may need manual cleanup."
+ )
diff --git a/tests/e2e/rayjob/rayjob_existing_cluster_test.py b/tests/e2e/rayjob/rayjob_existing_cluster_test.py
new file mode 100644
index 00000000..b62ea1ef
--- /dev/null
+++ b/tests/e2e/rayjob/rayjob_existing_cluster_test.py
@@ -0,0 +1,111 @@
+import pytest
+import sys
+import os
+from time import sleep
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+from support import *
+
+from codeflare_sdk import (
+ Cluster,
+ ClusterConfiguration,
+)
+from codeflare_sdk import RayJob, TokenAuthentication
+from codeflare_sdk.ray.rayjobs.status import CodeflareRayJobStatus
+from python_client.kuberay_job_api import RayjobApi
+
+
+class TestRayJobExistingCluster:
+ """Test RayJob against existing Kueue-managed clusters."""
+
+ def setup_method(self):
+ initialize_kubernetes_client(self)
+
+ def teardown_method(self):
+ delete_namespace(self)
+ delete_kueue_resources(self)
+
+ def test_existing_kueue_cluster(self):
+ """Test RayJob against Kueue-managed RayCluster."""
+ self.setup_method()
+ create_namespace(self)
+ create_kueue_resources(self)
+
+ cluster_name = "kueue-cluster"
+
+ if is_openshift():
+ auth = TokenAuthentication(
+ token=run_oc_command(["whoami", "--show-token=true"]),
+ server=run_oc_command(["whoami", "--show-server=true"]),
+ skip_tls=True,
+ )
+ auth.login()
+
+ cluster = Cluster(
+ ClusterConfiguration(
+ name=cluster_name,
+ namespace=self.namespace,
+ num_workers=1,
+ head_cpu_requests="500m",
+ head_cpu_limits="500m",
+ worker_cpu_requests=1,
+ worker_cpu_limits=1,
+ worker_memory_requests=1,
+ worker_memory_limits=4,
+ image=get_ray_image(),
+ local_queue=self.local_queues[0],
+ write_to_file=True,
+ verify_tls=False,
+ )
+ )
+
+ cluster.apply()
+ sleep(20)
+
+ # RayJob with explicit local_queue
+ rayjob_explicit = RayJob(
+ job_name="job-explicit-queue",
+ cluster_name=cluster_name,
+ namespace=self.namespace,
+ entrypoint="python -c \"import ray; ray.init(); print('Job with explicit queue')\"",
+ runtime_env={"env_vars": get_setup_env_variables(ACCELERATOR="cpu")},
+ local_queue=self.local_queues[0],
+ )
+
+ # RayJob using default queue
+ rayjob_default = RayJob(
+ job_name="job-default-queue",
+ cluster_name=cluster_name,
+ namespace=self.namespace,
+ entrypoint="python -c \"import ray; ray.init(); print('Job with default queue')\"",
+ runtime_env={"env_vars": get_setup_env_variables(ACCELERATOR="cpu")},
+ )
+
+ try:
+ # Test RayJob with explicit queue
+ assert rayjob_explicit.submit() == "job-explicit-queue"
+ self._wait_completion(rayjob_explicit)
+
+ # Test RayJob with default queue
+ assert rayjob_default.submit() == "job-default-queue"
+ self._wait_completion(rayjob_default)
+ finally:
+ rayjob_explicit.delete()
+ rayjob_default.delete()
+ cluster.down()
+
+ def _wait_completion(self, rayjob: RayJob, timeout: int = 600):
+ """Wait for RayJob completion."""
+ elapsed = 0
+ interval = 10
+
+ while elapsed < timeout:
+ status, _ = rayjob.status(print_to_console=False)
+ if status == CodeflareRayJobStatus.COMPLETE:
+ return
+ elif status == CodeflareRayJobStatus.FAILED:
+ raise AssertionError(f"RayJob '{rayjob.name}' failed")
+ sleep(interval)
+ elapsed += interval
+
+ raise TimeoutError(f"RayJob '{rayjob.name}' timeout after {timeout}s")
diff --git a/tests/e2e/rayjob/rayjob_lifecycled_cluster_test.py b/tests/e2e/rayjob/rayjob_lifecycled_cluster_test.py
new file mode 100644
index 00000000..51c72df6
--- /dev/null
+++ b/tests/e2e/rayjob/rayjob_lifecycled_cluster_test.py
@@ -0,0 +1,163 @@
+import pytest
+import sys
+import os
+from time import sleep
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+from support import *
+
+from codeflare_sdk import RayJob, ManagedClusterConfig
+from python_client.kuberay_job_api import RayjobApi
+from python_client.kuberay_cluster_api import RayClusterApi
+
+
+class TestRayJobLifecycledCluster:
+ """Test RayJob with auto-created cluster lifecycle management."""
+
+ def setup_method(self):
+ initialize_kubernetes_client(self)
+
+ def teardown_method(self):
+ delete_namespace(self)
+ delete_kueue_resources(self)
+
+ def test_lifecycled_kueue_managed(self):
+ """Test RayJob with Kueue-managed lifecycled cluster."""
+ self.setup_method()
+ create_namespace(self)
+ create_kueue_resources(self)
+
+ self.job_api = RayjobApi()
+ cluster_api = RayClusterApi()
+ job_name = "kueue-lifecycled"
+
+ # Get platform-appropriate resource configurations
+ resources = get_platform_appropriate_resources()
+
+ cluster_config = ManagedClusterConfig(
+ head_cpu_requests=resources["head_cpu_requests"],
+ head_cpu_limits=resources["head_cpu_limits"],
+ head_memory_requests=resources["head_memory_requests"],
+ head_memory_limits=resources["head_memory_limits"],
+ num_workers=1,
+ worker_cpu_requests=resources["worker_cpu_requests"],
+ worker_cpu_limits=resources["worker_cpu_limits"],
+ worker_memory_requests=resources["worker_memory_requests"],
+ worker_memory_limits=resources["worker_memory_limits"],
+ )
+
+ rayjob = RayJob(
+ job_name=job_name,
+ namespace=self.namespace,
+ cluster_config=cluster_config,
+ entrypoint="python -c \"import ray; ray.init(); print('Kueue job done')\"",
+ runtime_env={"env_vars": get_setup_env_variables(ACCELERATOR="cpu")},
+ local_queue=self.local_queues[0],
+ )
+
+ try:
+ assert rayjob.submit() == job_name
+ assert self.job_api.wait_until_job_running(
+ name=rayjob.name, k8s_namespace=rayjob.namespace, timeout=600
+ )
+
+ assert self.job_api.wait_until_job_finished(
+ name=rayjob.name, k8s_namespace=rayjob.namespace, timeout=300
+ )
+ finally:
+ try:
+ rayjob.delete()
+ except Exception:
+ pass # Job might already be deleted
+ verify_rayjob_cluster_cleanup(cluster_api, rayjob.name, rayjob.namespace)
+
+ def test_lifecycled_kueue_resource_queueing(self):
+ """Test Kueue resource queueing with lifecycled clusters."""
+ self.setup_method()
+ create_namespace(self)
+ create_limited_kueue_resources(self)
+
+ self.job_api = RayjobApi()
+ cluster_api = RayClusterApi()
+
+ # Get platform-appropriate resource configurations
+ resources = get_platform_appropriate_resources()
+
+ cluster_config = ManagedClusterConfig(
+ head_cpu_requests=resources["head_cpu_requests"],
+ head_cpu_limits=resources["head_cpu_limits"],
+ head_memory_requests=resources["head_memory_requests"],
+ head_memory_limits=resources["head_memory_limits"],
+ num_workers=0,
+ )
+
+ job1 = None
+ job2 = None
+ try:
+ job1 = RayJob(
+ job_name="holder",
+ namespace=self.namespace,
+ cluster_config=cluster_config,
+ entrypoint='python -c "import ray; import time; ray.init(); time.sleep(15)"',
+ runtime_env={"env_vars": get_setup_env_variables(ACCELERATOR="cpu")},
+ local_queue=self.local_queues[0],
+ )
+ assert job1.submit() == "holder"
+ assert self.job_api.wait_until_job_running(
+ name=job1.name, k8s_namespace=job1.namespace, timeout=60
+ )
+
+ job2 = RayJob(
+ job_name="waiter",
+ namespace=self.namespace,
+ cluster_config=cluster_config,
+ entrypoint='python -c "import ray; ray.init()"',
+ runtime_env={"env_vars": get_setup_env_variables(ACCELERATOR="cpu")},
+ local_queue=self.local_queues[0],
+ )
+ assert job2.submit() == "waiter"
+
+ # Wait for Kueue to process the job
+ sleep(5)
+ job2_cr = self.job_api.get_job(name=job2.name, k8s_namespace=job2.namespace)
+
+ # For RayJobs with managed clusters, check if Kueue is holding resources
+ job2_status = job2_cr.get("status", {})
+ ray_cluster_name = job2_status.get("rayClusterName", "")
+
+ # If RayCluster is not created yet, it means Kueue is holding the job
+ if not ray_cluster_name:
+ # This is the expected behavior
+ job_is_queued = True
+ else:
+ # Check RayCluster resources - if all are 0, it's queued
+ ray_cluster_status = job2_status.get("rayClusterStatus", {})
+ desired_cpu = ray_cluster_status.get("desiredCPU", "0")
+ desired_memory = ray_cluster_status.get("desiredMemory", "0")
+
+ # Kueue creates the RayCluster but with 0 resources when queued
+ job_is_queued = desired_cpu == "0" and desired_memory == "0"
+
+ assert job_is_queued, "Job2 should be queued by Kueue while Job1 is running"
+
+ assert self.job_api.wait_until_job_finished(
+ name=job1.name, k8s_namespace=job1.namespace, timeout=60
+ )
+
+ assert wait_for_kueue_admission(
+ self, self.job_api, job2.name, job2.namespace, timeout=30
+ )
+
+ assert self.job_api.wait_until_job_finished(
+ name=job2.name, k8s_namespace=job2.namespace, timeout=60
+ )
+ finally:
+ for job in [job1, job2]:
+ if job:
+ try:
+ job.delete()
+ verify_rayjob_cluster_cleanup(
+ cluster_api, job.name, job.namespace
+ )
+ except:
+ pass
diff --git a/tests/e2e/support.py b/tests/e2e/support.py
index fe9261a2..85b3dd35 100644
--- a/tests/e2e/support.py
+++ b/tests/e2e/support.py
@@ -2,6 +2,7 @@
import random
import string
import subprocess
+from time import sleep
from codeflare_sdk import get_cluster
from kubernetes import client, config
from codeflare_sdk.common.kubernetes_cluster.kube_api_helpers import (
@@ -27,11 +28,82 @@ def get_ray_cluster(cluster_name, namespace):
raise
+def is_openshift():
+ """Detect if running on OpenShift by checking for OpenShift-specific API resources."""
+ try:
+ api = client.ApiClient()
+ discovery = client.ApisApi(api)
+ # Check for OpenShift-specific API group
+ groups = discovery.get_api_versions().groups
+ for group in groups:
+ if group.name == "image.openshift.io":
+ return True
+ return False
+ except Exception:
+ # If we can't determine, assume it's not OpenShift
+ return False
+
+
def get_ray_image():
- return os.getenv(
- "RAY_IMAGE",
- get_ray_image_for_python_version(warn_on_unsupported=False),
- )
+ """
+ Get appropriate Ray image based on platform (OpenShift vs Kind/vanilla K8s).
+
+ The tests marked with @pytest.mark.openshift can run on both OpenShift and Kind clusters
+ with Kueue installed. This function automatically selects the appropriate image:
+ - OpenShift: Uses the CUDA runtime image (quay.io/modh/ray:...)
+ - Kind/K8s: Uses the standard Ray image (rayproject/ray:VERSION)
+
+ You can override this behavior by setting the RAY_IMAGE environment variable.
+ """
+ # Allow explicit override via environment variable
+ if "RAY_IMAGE" in os.environ:
+ return os.environ["RAY_IMAGE"]
+
+ # Auto-detect platform and return appropriate image
+ if is_openshift():
+ return get_ray_image_for_python_version()
+ else:
+ # Use standard Ray image for Kind/vanilla K8s
+ return f"rayproject/ray:{constants.RAY_VERSION}"
+
+
+def get_platform_appropriate_resources():
+ """
+ Get appropriate resource configurations based on platform.
+
+ OpenShift with MODH images requires more memory than Kind with standard Ray images.
+
+ Returns:
+ dict: Resource configurations with keys:
+ - head_cpu_requests, head_cpu_limits
+ - head_memory_requests, head_memory_limits
+ - worker_cpu_requests, worker_cpu_limits
+ - worker_memory_requests, worker_memory_limits
+ """
+ if is_openshift():
+ # MODH runtime images require more memory
+ return {
+ "head_cpu_requests": "1",
+ "head_cpu_limits": "1.5",
+ "head_memory_requests": 7,
+ "head_memory_limits": 8,
+ "worker_cpu_requests": "1",
+ "worker_cpu_limits": "1",
+ "worker_memory_requests": 5,
+ "worker_memory_limits": 6,
+ }
+ else:
+ # Standard Ray images require less memory
+ return {
+ "head_cpu_requests": "1",
+ "head_cpu_limits": "1.5",
+ "head_memory_requests": 7,
+ "head_memory_limits": 8,
+ "worker_cpu_requests": "1",
+ "worker_cpu_limits": "1",
+ "worker_memory_requests": 2,
+ "worker_memory_limits": 3,
+ }
def get_setup_env_variables(**kwargs):
@@ -147,6 +219,17 @@ def run_oc_command(args):
return None
+def run_kubectl_command(args):
+ try:
+ result = subprocess.run(
+ ["kubectl"] + args, capture_output=True, text=True, check=True
+ )
+ return result.stdout.strip()
+ except subprocess.CalledProcessError as e:
+ print(f"Error executing 'kubectl {' '.join(args)}': {e}")
+ return None
+
+
def create_cluster_queue(self, cluster_queue, flavor):
cluster_queue_json = {
"apiVersion": "kueue.x-k8s.io/v1beta1",
@@ -161,9 +244,9 @@ def create_cluster_queue(self, cluster_queue, flavor):
{
"name": flavor,
"resources": [
- {"name": "cpu", "nominalQuota": 9},
- {"name": "memory", "nominalQuota": "36Gi"},
- {"name": "nvidia.com/gpu", "nominalQuota": 1},
+ {"name": "cpu", "nominalQuota": 20},
+ {"name": "memory", "nominalQuota": "80Gi"},
+ {"name": "nvidia.com/gpu", "nominalQuota": 2},
],
},
],
@@ -301,7 +384,6 @@ def create_kueue_resources(
def delete_kueue_resources(self):
- # Delete if given cluster-queue exists
for cq in self.cluster_queues:
try:
self.custom_api.delete_cluster_custom_object(
@@ -409,3 +491,234 @@ def assert_get_cluster_and_jobsubmit(
assert job_list[0].submission_id == submission_id
cluster.down()
+
+
+def wait_for_kueue_admission(self, job_api, job_name, namespace, timeout=120):
+ print(f"Waiting for Kueue admission of job '{job_name}'...")
+ elapsed_time = 0
+ check_interval = 5
+
+ while elapsed_time < timeout:
+ try:
+ job_cr = job_api.get_job(name=job_name, k8s_namespace=namespace)
+
+ # Check if the job is no longer suspended
+ is_suspended = job_cr.get("spec", {}).get("suspend", False)
+
+ if not is_suspended:
+ print(f"✓ Job '{job_name}' admitted by Kueue (no longer suspended)")
+ return True
+
+ # Debug: Check workload status every 10 seconds
+ if elapsed_time % 10 == 0:
+ workload = get_kueue_workload_for_job(self, job_name, namespace)
+ if workload:
+ conditions = workload.get("status", {}).get("conditions", [])
+ print(f" DEBUG: Workload conditions for '{job_name}':")
+ for condition in conditions:
+ print(
+ f" - {condition.get('type')}: {condition.get('status')} - {condition.get('reason', '')} - {condition.get('message', '')}"
+ )
+
+ # Optional: Check status conditions for more details
+ conditions = job_cr.get("status", {}).get("conditions", [])
+ for condition in conditions:
+ if (
+ condition.get("type") == "Suspended"
+ and condition.get("status") == "False"
+ ):
+ print(
+ f"✓ Job '{job_name}' admitted by Kueue (Suspended=False condition)"
+ )
+ return True
+
+ except Exception as e:
+ print(f"Error checking job status: {e}")
+
+ sleep(check_interval)
+ elapsed_time += check_interval
+
+ print(f"✗ Timeout waiting for Kueue admission of job '{job_name}'")
+ return False
+
+
+def create_limited_kueue_resources(self):
+ print("Creating limited Kueue resources for preemption testing...")
+
+ # Create a resource flavor with default (no special labels/tolerations)
+ resource_flavor = f"limited-flavor-{random_choice()}"
+ create_resource_flavor(
+ self, resource_flavor, default=True, with_labels=False, with_tolerations=False
+ )
+ self.resource_flavors = [resource_flavor]
+
+ # Create a cluster queue with very limited resources
+ # Adjust quota based on platform - OpenShift needs more memory
+ if is_openshift():
+ # MODH images need more memory, so higher quota but still limited to allow only 1 job
+ cpu_quota = 3
+ memory_quota = "15Gi" # One job needs ~8Gi head, allow some buffer
+ else:
+ # Standard Ray images - one job needs ~8G head + 500m submitter
+ cpu_quota = 3
+ memory_quota = "10Gi" # Enough for one job (8G head + submitter), but not two
+
+ cluster_queue_name = f"limited-cq-{random_choice()}"
+ cluster_queue_json = {
+ "apiVersion": "kueue.x-k8s.io/v1beta1",
+ "kind": "ClusterQueue",
+ "metadata": {"name": cluster_queue_name},
+ "spec": {
+ "namespaceSelector": {},
+ "resourceGroups": [
+ {
+ "coveredResources": ["cpu", "memory"],
+ "flavors": [
+ {
+ "name": resource_flavor,
+ "resources": [
+ {
+ "name": "cpu",
+ "nominalQuota": cpu_quota,
+ },
+ {
+ "name": "memory",
+ "nominalQuota": memory_quota,
+ },
+ ],
+ }
+ ],
+ }
+ ],
+ },
+ }
+
+ try:
+ self.custom_api.create_cluster_custom_object(
+ group="kueue.x-k8s.io",
+ plural="clusterqueues",
+ version="v1beta1",
+ body=cluster_queue_json,
+ )
+ print(f"✓ Created limited ClusterQueue: {cluster_queue_name}")
+ except Exception as e:
+ print(f"Error creating limited ClusterQueue: {e}")
+ raise
+
+ self.cluster_queues = [cluster_queue_name]
+
+ # Create a local queue
+ local_queue_name = f"limited-lq-{random_choice()}"
+ create_local_queue(self, cluster_queue_name, local_queue_name, is_default=True)
+ self.local_queues = [local_queue_name]
+
+ print("✓ Limited Kueue resources created successfully")
+
+
+def get_kueue_workload_for_job(self, job_name, namespace):
+ try:
+ # List all workloads in the namespace
+ workloads = self.custom_api.list_namespaced_custom_object(
+ group="kueue.x-k8s.io",
+ version="v1beta1",
+ plural="workloads",
+ namespace=namespace,
+ )
+
+ # Find workload with matching RayJob owner reference
+ for workload in workloads.get("items", []):
+ owner_refs = workload.get("metadata", {}).get("ownerReferences", [])
+
+ for owner_ref in owner_refs:
+ if (
+ owner_ref.get("kind") == "RayJob"
+ and owner_ref.get("name") == job_name
+ ):
+ workload_name = workload.get("metadata", {}).get("name")
+ print(
+ f"✓ Found Kueue workload '{workload_name}' for RayJob '{job_name}'"
+ )
+ return workload
+
+ print(f"✗ No Kueue workload found for RayJob '{job_name}'")
+ return None
+
+ except Exception as e:
+ print(f"Error getting Kueue workload for job '{job_name}': {e}")
+ return None
+
+
+def wait_for_job_status(
+ job_api, rayjob_name: str, namespace: str, expected_status: str, timeout: int = 30
+) -> bool:
+ """
+ Wait for a RayJob to reach a specific deployment status.
+
+ Args:
+ job_api: RayjobApi instance
+ rayjob_name: Name of the RayJob
+ namespace: Namespace of the RayJob
+ expected_status: Expected jobDeploymentStatus value
+ timeout: Maximum time to wait in seconds
+
+ Returns:
+ bool: True if status reached, False if timeout
+ """
+ elapsed_time = 0
+ check_interval = 2
+
+ while elapsed_time < timeout:
+ status = job_api.get_job_status(name=rayjob_name, k8s_namespace=namespace)
+ if status and status.get("jobDeploymentStatus") == expected_status:
+ return True
+
+ sleep(check_interval)
+ elapsed_time += check_interval
+
+ return False
+
+
+def verify_rayjob_cluster_cleanup(
+ cluster_api, rayjob_name: str, namespace: str, timeout: int = 60
+):
+ """
+ Verify that the RayCluster created by a RayJob has been cleaned up.
+ Handles KubeRay's automatic suffix addition to cluster names.
+
+ Args:
+ cluster_api: RayClusterApi instance
+ rayjob_name: Name of the RayJob
+ namespace: Namespace to check
+ timeout: Maximum time to wait in seconds
+
+ Raises:
+ TimeoutError: If cluster is not cleaned up within timeout
+ """
+ elapsed_time = 0
+ check_interval = 5
+
+ while elapsed_time < timeout:
+ # List all RayClusters in the namespace
+ clusters = cluster_api.list_ray_clusters(
+ k8s_namespace=namespace, async_req=False
+ )
+
+ # Check if any cluster exists that starts with our job name
+ found = False
+ for cluster in clusters.get("items", []):
+ cluster_name = cluster.get("metadata", {}).get("name", "")
+ # KubeRay creates clusters with pattern: {job_name}-raycluster-{suffix}
+ if cluster_name.startswith(f"{rayjob_name}-raycluster"):
+ found = True
+ break
+
+ if not found:
+ # No cluster found, cleanup successful
+ return
+
+ sleep(check_interval)
+ elapsed_time += check_interval
+
+ raise TimeoutError(
+ f"RayCluster for job '{rayjob_name}' was not cleaned up within {timeout} seconds"
+ )
diff --git a/tests/test_cluster_yamls/appwrapper/unit-test-all-params.yaml b/tests/test_cluster_yamls/appwrapper/unit-test-all-params.yaml
index aa097dd9..fe07e331 100644
--- a/tests/test_cluster_yamls/appwrapper/unit-test-all-params.yaml
+++ b/tests/test_cluster_yamls/appwrapper/unit-test-all-params.yaml
@@ -19,6 +19,7 @@ spec:
controller-tools.k8s.io: '1.0'
key1: value1
key2: value2
+ ray.io/cluster: aw-all-params
name: aw-all-params
namespace: ns
spec:
diff --git a/tests/test_cluster_yamls/kueue/aw_kueue.yaml b/tests/test_cluster_yamls/kueue/aw_kueue.yaml
index f5f16406..92e5078d 100644
--- a/tests/test_cluster_yamls/kueue/aw_kueue.yaml
+++ b/tests/test_cluster_yamls/kueue/aw_kueue.yaml
@@ -13,6 +13,7 @@ spec:
metadata:
labels:
controller-tools.k8s.io: '1.0'
+ ray.io/cluster: unit-test-aw-kueue
name: unit-test-aw-kueue
namespace: ns
spec:
diff --git a/tests/test_cluster_yamls/kueue/ray_cluster_kueue.yaml b/tests/test_cluster_yamls/kueue/ray_cluster_kueue.yaml
index d6db4f4a..04331aed 100644
--- a/tests/test_cluster_yamls/kueue/ray_cluster_kueue.yaml
+++ b/tests/test_cluster_yamls/kueue/ray_cluster_kueue.yaml
@@ -13,6 +13,7 @@ spec:
metadata:
labels:
controller-tools.k8s.io: '1.0'
+ ray.io/cluster: unit-test-cluster-kueue
name: unit-test-cluster-kueue
namespace: ns
spec:
diff --git a/tests/test_cluster_yamls/ray/default-appwrapper.yaml b/tests/test_cluster_yamls/ray/default-appwrapper.yaml
index 27828163..1041f3b5 100644
--- a/tests/test_cluster_yamls/ray/default-appwrapper.yaml
+++ b/tests/test_cluster_yamls/ray/default-appwrapper.yaml
@@ -11,6 +11,7 @@ spec:
metadata:
labels:
controller-tools.k8s.io: '1.0'
+ ray.io/cluster: default-appwrapper
name: default-appwrapper
namespace: ns
spec:
diff --git a/tests/test_cluster_yamls/ray/default-ray-cluster.yaml b/tests/test_cluster_yamls/ray/default-ray-cluster.yaml
index d2e6b05a..213b22cf 100644
--- a/tests/test_cluster_yamls/ray/default-ray-cluster.yaml
+++ b/tests/test_cluster_yamls/ray/default-ray-cluster.yaml
@@ -3,6 +3,7 @@ kind: RayCluster
metadata:
labels:
controller-tools.k8s.io: '1.0'
+ ray.io/cluster: default-cluster
name: default-cluster
namespace: ns
spec:
diff --git a/tests/test_cluster_yamls/ray/unit-test-all-params.yaml b/tests/test_cluster_yamls/ray/unit-test-all-params.yaml
index ee0878c1..7c7d82d6 100644
--- a/tests/test_cluster_yamls/ray/unit-test-all-params.yaml
+++ b/tests/test_cluster_yamls/ray/unit-test-all-params.yaml
@@ -10,6 +10,7 @@ metadata:
key1: value1
key2: value2
kueue.x-k8s.io/queue-name: local-queue-default
+ ray.io/cluster: test-all-params
name: test-all-params
namespace: ns
spec: